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Explore University of Southampton modules. Search our module catalogue to find any course module available for undergraduate or postgraduate study.

9130 modules

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BIOL2013 (2026-27)

Bioinformatics

The module includes an introduction to bioinformatics and its role in modern 'Omics' technologies; developments in DNA sequencing technologies; bioinformatic analyses of DNA; sequence alignment and biological databases.
BIOL3063 (2031-32)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale 'systems' biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL6074 (2026-27)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL6074 (2028-29)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL6074 (2029-30)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL3063 (2027-28)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale 'systems' biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL3063 (2028-29)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale 'systems' biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL3063 (2029-30)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale 'systems' biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL3063 (2030-31)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale 'systems' biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL6074 (2030-31)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
BIOL6074 (2025-26)

Bioinformatics and Systems Biology

Large-scale approaches at the molecular, cellular, organismal and ecological level are revolutionizing biology by enabling systems-level questions to be addressed. In many cases, these approaches are driven by technologies that allow the components of biological systems to be surveyed en masse. For example, whole genome sequencing can rapidly profile complete human genomes and transcriptomic analyses provide quantitative surveys of 1000s of RNA molecules. In addition to changing fundamental biology, these techniques are a central component of personalized medicine by providing molecular readouts for individual patients to improve both diagnosis and therapy. Interpreting the outcome of these large-scale experiments requires an understanding of the experimental technologies themselves as well as the underlying biological processes. Bioinformatics techniques address many of the challenges of these experiments including how to process, analyse, visualize and ultimately interpret the data. This module will introduce students to large-scale ‘systems’ biology as well as equipping them with the practical, hands-on skills necessary to fully utilize data resulting from these techniques.
SOES6092 (2026-27)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2028-29)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2029-30)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2030-31)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2025-26)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2029-30)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SOES6092 (2028-29)

Biological Data Science in R

Biological data science is a rapidly evolving field at the intersection of biology, statistics, and computer science. There is a growing demand for professionals skilled in analysing and interpreting data as well as an expectation that students will be familiar with responsible use of Artificial Intelligence to achieve this goal. Upon successful completion of this course, students will have the skills and knowledge necessary to effectively analyse and interpret biological data using the versatile and freely available programming language R, empowering them to contribute to scientific and societal advancements.
SESM6052 (2025-26)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
SESM6052 (2026-27)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
SESM6052 (2025-26)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
SESM6052 (2026-27)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
SESM6052 (2028-29)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
SESM6052 (2029-30)

Biological Materials and Biomedical Devices

This module provides an introduction to the ways in which engineering methods are used in characterising and modelling the biophysical properties of tissues and organs, and applied to the design of biomedical implants and devices for the treatment of a wide range of human diseases and dysfunctions. In particular, you will learn about the design and function of prosthetic limbs, auditory implants, cardiovascular devices, and how regenerative medicine and tissue engineering are influencing the development of biomedical implants. The relevant background to anatomy, disease, injury, continuum biomechanics and the mechanical properties of biological materials will be covered such that engineering solutions can be presented with respect to the associated clinical needs. In addition to lectures, you will have hands-on lab-based tasks and talks by specialist clinicians.
BIOL2042 (2026-27)

Biological Sciences Study Abroad

BIOL2042 (2027-28)

Biological Sciences Study Abroad

BIOM1000 (2025-26)

BIOM Part One Laboratory Programme

This module is the lab programme for all first-year students enrolled on the BIOM degree programme. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career.

Structurally, the BIOM Part One Laboratory Programme is organised to cover all practical and laboratory based work in the first year of study on all BIOM Pathways in a single timetable organised into central laboratory locations.

The module is structured into a series of activities. There are a series of general sessions which all students enrolled on this module are expected to attempt:

•Information lectures.
•Transferable skills laboratories
•Professional skills laboratories.
•Assignments.

In addition, there are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.
BIOM1000 (2025-26)

BIOM Part One Laboratory Programme

This module is the lab programme for all first-year students enrolled on the BIOM degree programme. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career.

Structurally, the BIOM Part One Laboratory Programme is organised to cover all practical and laboratory based work in the first year of study on all BIOM Pathways in a single timetable organised into central laboratory locations.

The module is structured into a series of activities. There are a series of general sessions which all students enrolled on this module are expected to attempt:

•Information lectures.
•Transferable skills laboratories
•Professional skills laboratories.
•Assignments.

In addition, there are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.
BIOM1000 (2026-27)

BIOM Part One Laboratory Programme

This module is the lab programme for all first-year students enrolled on the BIOM degree programme. It aims to give students the opportunity to apply the theory that they learn in their other modules, and to provide them with transferrable, subject-based and professional skills that they will need for their degree and career.

Structurally, the BIOM Part One Laboratory Programme is organised to cover all practical and laboratory based work in the first year of study on all BIOM Pathways in a single timetable organised into central laboratory locations.

The module is structured into a series of activities. There are a series of general sessions which all students enrolled on this module are expected to attempt:

•Information lectures.
•Transferable skills laboratories
•Professional skills laboratories.
•Assignments.

In addition, there are a number of technical laboratories integrated into the Laboratory Programme which cover practical Learning Outcomes from other technical modules in the Programmes.
SESM3028 (2026-27)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
SESM3028 (2027-28)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
SESM3028 (2028-29)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
SESM3028 (2027-28)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
SESM3028 (2025-26)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
SESM3028 (2025-26)

Biomaterials

A biomaterial can be described as a material used in a biomedical device intended to interact with biological systems. The selection of an appropriate biomaterial is critical to the performance of an implant. For a hip replacement, properties such as good strength, excellent corrosion resistance, fatigue resistance and biocompatibility are required to ensure the hip replacement does not fail in service. In this module, you will learn about the various polymer, metal and ceramic based materials used as biomaterials, and discover why these materials have been accepted into clinical practice. A series of case studies will be used as examples to show how past failures have led to the materials that are used today, in particular, focussing on hip and knee replacements.
BIOM2008 (2027-28)

Biomechatronics

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.
BIOM2008 (2026-27)

Biomechatronics

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.
ISVR6138 (2025-26)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2028-29)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2025-26)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2029-30)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2026-27)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2027-28)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2028-29)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2029-30)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2030-31)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
ISVR6138 (2030-31)

Biomedical Application of Signal and Image Processing

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.
BIOM1004 (2025-26)

Biomedical Engineering Design

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.
ELEC1212 (2025-26)

Biomedical Engineering Design

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.
BIOM1004 (2026-27)

Biomedical Engineering Design

This module teaches the applications of biomedical signal analysis and control systems for biomedicine. The module emphasises developing an understanding through lab-based system design exercises by applying theoretical knowledge taught in the module. The module is split in two parts: 50% control and 50% biomedical signals analysis.
The control topics include electrical/mechanical analogues, p notation, block diagrams, electromechanical systems: torque, inertia, motor model. Using this knowledge, you will follow the Stanford bio-design process to develop an Active Tremor Suppression Brace for Parkinsons
The biomedical signals analysis part will provide a theoretical understanding of the fundamentals of biomedical signal processing, including representation of signals, signal arithmetics, frequency analysis and time-frequency representation of a signal and the fundamentals of Electrocardiogram (ECG) signals. You will design an automated algorithm for ECG analysis in the lab where you will write programmes to separate artefacts and identify individual ECG waves which are fundamental in clinical diagnosis of cardiac diseases.