MSc — 2027 entry Physics

The module will provide students with practical skills and background knowledge needed to work in a radiation laboratory. Laboratory sessions are designed to provide the student with practical experience in handling radioactive substances, detectors and instrumentation.

This course starts with an overview of human biology, followed by a discussion of the nature of the interaction of ionising and non-ionising radiation with biological systems. The course emphasises the effects at the cellular level and the impact that this has on the individual and across the population. The behaviour and effects of ingested and inhaled radionuclides are also covered.

This module introduces six advanced topics in physics. Students will be assessed on only four of these topics, with individuals self-selecting the contact sessions, coursework options and exam questions that reflect their preference. An indicative list of these topics include: biological physics, special relativity, particle physics, cosmology, nuclear astrophysics, and quantum computing.

This module presents an introduction to key topics in radiation physics, the interactions of radiation with matter, and the principles of radiation measurement and dosimetry. The module also covers introductory material describing the function and operation of radiation detectors.

The module provides a graduate level introduction to Atomic and Nuclear Physics. It is aimed at both students with a first degree in Physics (as a review) and also as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in quantum mechanics and atomic and nuclear structure.

Quantum computers are built around a central processing unit which is a physical device operating according to the rules of quantum mechanics. This module teaches the principles of how quantum processors are built and how they function. The module follows the superconducting architecture, which currently has a significant role in the industry and has been widely studied and implemented. The module will start with reviewing the basic physics required to understand superconducting qubits and the theory of superconducting circuits. It will then continue to the operation of such qubits to perform gates, storage, and readout. After studying the basic building blocks and operations, the module will discuss early demonstrations of important algorithms on superconducting processors. These will lead us naturally to the important topics of improving performance through protection of coherence, advanced control, and validation. The last part of the module will focus on state-of-the-art superconducting processors, focusing on the various challenges in scaling up and the strategies that the industry is pursuing to overcome them.

The first half of this module covers various applications of statistical physics to model share prices and financial markets. This mathematics is then applied to calculating prices for some examples of financial derivatives. The second half of the module then focusses on optimization problems with examples including logistics, aerospace, traffic control and finance (which includes pricing, risk managements and portfolio optimizations in financial markets). There is a particular focus on the role of quantum optimization and the use of quantum computer algorithms in finance.

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Radiations of various types are widely used for therapeutic purposes. The bulk of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to radiotherapy systems, for beam delivery, guidance and dosimetry. Around one-third of the module is then devoted to a number of key uses of non-ionising radiation in delivery of various therapies, with sources based on laser light, uv and high-intensity focused ultrasound.

Ionising radiation is widely used for diagnostic purposes, and multi-modality imaging is now becoming ubiquitous. The majority of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to imaging systems and image perception. Detailed lectures then cover X-radiography, X-ray computed tomography, radiopharmaceuticals, nuclear medicine. The lectures will be supported by an assessed nuclear medicine practical and by tutorials in image processing and image registration.

The module is designed to give students knowledge of the basic physics that underpins nuclear magnetic resonance imaging (NMR / MRI) and ultrasound, together with details of common imaging strategies.It delivers material on the basic principles of NMR and medical MRI . It also provides an introduction to ultrasound, a major non-ionising radiation imaging modality, with lectures complemented by three laboratory sessions.

This module aims to provide an advanced level understanding of explosive nuclear astrophysics and the physics of stars. In particular, the course will provide an analytical underpinning of resonant reaction rates, together with the experimental techniques involved in their determination, as well as a theoretical treatment of nuclear reactions and celestial objects.

This module will introduce the students to the principles and formalism of General Relativity and its applications to Black Holes and astrophysical phenomena.

The course provides an introduction to nuclear energy generation and applications of nuclear science. Nuclear reactors, their physics and operation are described. Nuclear reactor safety case work is also discussed. Future potential energy generation mechanisms such including nuclear fusion will be discussed. The module will also present a range of applications of radioactivity measurement including aspects of Environmental Science and Medical Diagnogstics and treatment therapy. The module will include some aspects of calculus and first order differential equations.

Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from modules in earlier years, but will provide a review at the beginning. Topics include a review of quantum mechanics, operator methods and applications to the harmonic oscillator, spin & angular momentum, symmetries in quantum mechanics. The second half of the module then moves beyond isolated quantum systems by addressing the topic of Quantum Entanglement and Quantum Coherence. Here the important concepts of entanglement and decoherence will be studied by developing an understanding of open quantum systems and the density matrix formalism.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

The module is aimed at giving students an understanding of the use of computers in the broader context of medical physics and radiation protection. This will range from the use of Monte Carlo modelling using TOPAS for dosimetry and experimental design, to the use of Python to code simple problems related to image processing and data science. In addition, they will learn the importance of data security and data governance with specific application to a clinical context.

The module provides a Level 6 (final year) introduction to the sources of radioactive materials, how they are produced and their accurate measurement. This module will also give detailed information on the sources and uses of radioactive materials for environmental, medical and industrial medical applications. The is aimed at both students studying at Level 6 or 7 in Physical Science (Physics, Chemistry etc,.) or related Engineering (e.g. Chemical or Environmental Engineering) subject. It will also serve as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in the measurement and characterization of radioactive sources.

Quantum machine learning (QML) is an emerging field at the intersection of quantum computing and data science, exploring how quantum systems can enhance learning, optimisation, and data analysis tasks. At the same time, quantum simulation remains one of the most promising near-term applications of quantum computers, enabling the study of complex quantum systems that are intractable for classical computation. This module provides an integrated introduction to both quantum machine learning and quantum simulation. It develops an understanding of how quantum algorithms and variational methods can be applied to learning and optimisation problems, alongside the theoretical and practical frameworks required to simulate physical systems on quantum hardware. Emphasis is placed on mapping real-world problems to quantum representations, understanding algorithmic design, and evaluating performance under realistic hardware constraints.

The module will provide students with practical skills and background knowledge needed to work in a radiation laboratory. Laboratory sessions are designed to provide the student with practical experience in handling radioactive substances, detectors and instrumentation.

This course starts with an overview of human biology, followed by a discussion of the nature of the interaction of ionising and non-ionising radiation with biological systems. The course emphasises the effects at the cellular level and the impact that this has on the individual and across the population. The behaviour and effects of ingested and inhaled radionuclides are also covered.

This module introduces six advanced topics in physics. Students will be assessed on only four of these topics, with individuals self-selecting the contact sessions, coursework options and exam questions that reflect their preference. An indicative list of these topics include: biological physics, special relativity, particle physics, cosmology, nuclear astrophysics, and quantum computing.

This module presents an introduction to key topics in radiation physics, the interactions of radiation with matter, and the principles of radiation measurement and dosimetry. The module also covers introductory material describing the function and operation of radiation detectors.

The module provides a graduate level introduction to Atomic and Nuclear Physics. It is aimed at both students with a first degree in Physics (as a review) and also as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in quantum mechanics and atomic and nuclear structure.

Quantum computers are built around a central processing unit which is a physical device operating according to the rules of quantum mechanics. This module teaches the principles of how quantum processors are built and how they function. The module follows the superconducting architecture, which currently has a significant role in the industry and has been widely studied and implemented. The module will start with reviewing the basic physics required to understand superconducting qubits and the theory of superconducting circuits. It will then continue to the operation of such qubits to perform gates, storage, and readout. After studying the basic building blocks and operations, the module will discuss early demonstrations of important algorithms on superconducting processors. These will lead us naturally to the important topics of improving performance through protection of coherence, advanced control, and validation. The last part of the module will focus on state-of-the-art superconducting processors, focusing on the various challenges in scaling up and the strategies that the industry is pursuing to overcome them.

The first half of this module covers various applications of statistical physics to model share prices and financial markets. This mathematics is then applied to calculating prices for some examples of financial derivatives. The second half of the module then focusses on optimization problems with examples including logistics, aerospace, traffic control and finance (which includes pricing, risk managements and portfolio optimizations in financial markets). There is a particular focus on the role of quantum optimization and the use of quantum computer algorithms in finance.

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Radiations of various types are widely used for therapeutic purposes. The bulk of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to radiotherapy systems, for beam delivery, guidance and dosimetry. Around one-third of the module is then devoted to a number of key uses of non-ionising radiation in delivery of various therapies, with sources based on laser light, uv and high-intensity focused ultrasound.

Ionising radiation is widely used for diagnostic purposes, and multi-modality imaging is now becoming ubiquitous. The majority of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to imaging systems and image perception. Detailed lectures then cover X-radiography, X-ray computed tomography, radiopharmaceuticals, nuclear medicine. The lectures will be supported by an assessed nuclear medicine practical and by tutorials in image processing and image registration.

The module is designed to give students knowledge of the basic physics that underpins nuclear magnetic resonance imaging (NMR / MRI) and ultrasound, together with details of common imaging strategies.It delivers material on the basic principles of NMR and medical MRI . It also provides an introduction to ultrasound, a major non-ionising radiation imaging modality, with lectures complemented by three laboratory sessions.

This module aims to provide an advanced level understanding of explosive nuclear astrophysics and the physics of stars. In particular, the course will provide an analytical underpinning of resonant reaction rates, together with the experimental techniques involved in their determination, as well as a theoretical treatment of nuclear reactions and celestial objects.

This module will introduce the students to the principles and formalism of General Relativity and its applications to Black Holes and astrophysical phenomena.

The course provides an introduction to nuclear energy generation and applications of nuclear science. Nuclear reactors, their physics and operation are described. Nuclear reactor safety case work is also discussed. Future potential energy generation mechanisms such including nuclear fusion will be discussed. The module will also present a range of applications of radioactivity measurement including aspects of Environmental Science and Medical Diagnogstics and treatment therapy. The module will include some aspects of calculus and first order differential equations.

Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from modules in earlier years, but will provide a review at the beginning. Topics include a review of quantum mechanics, operator methods and applications to the harmonic oscillator, spin & angular momentum, symmetries in quantum mechanics. The second half of the module then moves beyond isolated quantum systems by addressing the topic of Quantum Entanglement and Quantum Coherence. Here the important concepts of entanglement and decoherence will be studied by developing an understanding of open quantum systems and the density matrix formalism.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

The module is aimed at giving students an understanding of the use of computers in the broader context of medical physics and radiation protection. This will range from the use of Monte Carlo modelling using TOPAS for dosimetry and experimental design, to the use of Python to code simple problems related to image processing and data science. In addition, they will learn the importance of data security and data governance with specific application to a clinical context.

The module provides a Level 6 (final year) introduction to the sources of radioactive materials, how they are produced and their accurate measurement. This module will also give detailed information on the sources and uses of radioactive materials for environmental, medical and industrial medical applications. The is aimed at both students studying at Level 6 or 7 in Physical Science (Physics, Chemistry etc,.) or related Engineering (e.g. Chemical or Environmental Engineering) subject. It will also serve as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in the measurement and characterization of radioactive sources.

Quantum machine learning (QML) is an emerging field at the intersection of quantum computing and data science, exploring how quantum systems can enhance learning, optimisation, and data analysis tasks. At the same time, quantum simulation remains one of the most promising near-term applications of quantum computers, enabling the study of complex quantum systems that are intractable for classical computation. This module provides an integrated introduction to both quantum machine learning and quantum simulation. It develops an understanding of how quantum algorithms and variational methods can be applied to learning and optimisation problems, alongside the theoretical and practical frameworks required to simulate physical systems on quantum hardware. Emphasis is placed on mapping real-world problems to quantum representations, understanding algorithmic design, and evaluating performance under realistic hardware constraints.

The module will provide students with practical skills and background knowledge needed to work in a radiation laboratory. Laboratory sessions are designed to provide the student with practical experience in handling radioactive substances, detectors and instrumentation.

This course starts with an overview of human biology, followed by a discussion of the nature of the interaction of ionising and non-ionising radiation with biological systems. The course emphasises the effects at the cellular level and the impact that this has on the individual and across the population. The behaviour and effects of ingested and inhaled radionuclides are also covered.

This module introduces six advanced topics in physics. Students will be assessed on only four of these topics, with individuals self-selecting the contact sessions, coursework options and exam questions that reflect their preference. An indicative list of these topics include: biological physics, special relativity, particle physics, cosmology, nuclear astrophysics, and quantum computing.

This module presents an introduction to key topics in radiation physics, the interactions of radiation with matter, and the principles of radiation measurement and dosimetry. The module also covers introductory material describing the function and operation of radiation detectors.

The module provides a graduate level introduction to Atomic and Nuclear Physics. It is aimed at both students with a first degree in Physics (as a review) and also as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in quantum mechanics and atomic and nuclear structure.

Quantum computers are built around a central processing unit which is a physical device operating according to the rules of quantum mechanics. This module teaches the principles of how quantum processors are built and how they function. The module follows the superconducting architecture, which currently has a significant role in the industry and has been widely studied and implemented. The module will start with reviewing the basic physics required to understand superconducting qubits and the theory of superconducting circuits. It will then continue to the operation of such qubits to perform gates, storage, and readout. After studying the basic building blocks and operations, the module will discuss early demonstrations of important algorithms on superconducting processors. These will lead us naturally to the important topics of improving performance through protection of coherence, advanced control, and validation. The last part of the module will focus on state-of-the-art superconducting processors, focusing on the various challenges in scaling up and the strategies that the industry is pursuing to overcome them.

The first half of this module covers various applications of statistical physics to model share prices and financial markets. This mathematics is then applied to calculating prices for some examples of financial derivatives. The second half of the module then focusses on optimization problems with examples including logistics, aerospace, traffic control and finance (which includes pricing, risk managements and portfolio optimizations in financial markets). There is a particular focus on the role of quantum optimization and the use of quantum computer algorithms in finance.

Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics. This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised. This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.

Radiations of various types are widely used for therapeutic purposes. The bulk of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to radiotherapy systems, for beam delivery, guidance and dosimetry. Around one-third of the module is then devoted to a number of key uses of non-ionising radiation in delivery of various therapies, with sources based on laser light, uv and high-intensity focused ultrasound.

Ionising radiation is widely used for diagnostic purposes, and multi-modality imaging is now becoming ubiquitous. The majority of hospital physicists work with ionising radiation and hence the topic is fundamental for anyone entering the profession. In this module, an introduction is given to imaging systems and image perception. Detailed lectures then cover X-radiography, X-ray computed tomography, radiopharmaceuticals, nuclear medicine. The lectures will be supported by an assessed nuclear medicine practical and by tutorials in image processing and image registration.

The module is designed to give students knowledge of the basic physics that underpins nuclear magnetic resonance imaging (NMR / MRI) and ultrasound, together with details of common imaging strategies.It delivers material on the basic principles of NMR and medical MRI . It also provides an introduction to ultrasound, a major non-ionising radiation imaging modality, with lectures complemented by three laboratory sessions.

This module aims to provide an advanced level understanding of explosive nuclear astrophysics and the physics of stars. In particular, the course will provide an analytical underpinning of resonant reaction rates, together with the experimental techniques involved in their determination, as well as a theoretical treatment of nuclear reactions and celestial objects.

This module will introduce the students to the principles and formalism of General Relativity and its applications to Black Holes and astrophysical phenomena.

The course provides an introduction to nuclear energy generation and applications of nuclear science. Nuclear reactors, their physics and operation are described. Nuclear reactor safety case work is also discussed. Future potential energy generation mechanisms such including nuclear fusion will be discussed. The module will also present a range of applications of radioactivity measurement including aspects of Environmental Science and Medical Diagnogstics and treatment therapy. The module will include some aspects of calculus and first order differential equations.

Quantum Mechanics topics are essential building blocks for our understanding of many physical systems. The module assumes basic knowledge in quantum mechanics from modules in earlier years, but will provide a review at the beginning. Topics include a review of quantum mechanics, operator methods and applications to the harmonic oscillator, spin & angular momentum, symmetries in quantum mechanics. The second half of the module then moves beyond isolated quantum systems by addressing the topic of Quantum Entanglement and Quantum Coherence. Here the important concepts of entanglement and decoherence will be studied by developing an understanding of open quantum systems and the density matrix formalism.

This module covers circuit-based quantum computers and some of the algorithms that can be implemented using them.  The idea of a classical computational algorithm and its complexity is introduced, and used as a language to discuss quantum computational algorithms.  Components of quantum circuits are discussed individually, and then built up to show how algorithms such as the quantum Fourier transform and Grover¿s search algorithm can be implemented, and how such algorithms can be used in applications such as factorizing integers using Shor¿s algorithm.  A suitable computational frameworkis introduced and used as a tool to implement the algorithms on simulated quantum computers.

The module is aimed at giving students an understanding of the use of computers in the broader context of medical physics and radiation protection. This will range from the use of Monte Carlo modelling using TOPAS for dosimetry and experimental design, to the use of Python to code simple problems related to image processing and data science. In addition, they will learn the importance of data security and data governance with specific application to a clinical context.

The module provides a Level 6 (final year) introduction to the sources of radioactive materials, how they are produced and their accurate measurement. This module will also give detailed information on the sources and uses of radioactive materials for environmental, medical and industrial medical applications. The is aimed at both students studying at Level 6 or 7 in Physical Science (Physics, Chemistry etc,.) or related Engineering (e.g. Chemical or Environmental Engineering) subject. It will also serve as key, underpinning material for graduates with non-physics first degrees (e.g. Engineering, Chemistry, Mathematics etc,) to provide a foundation in the measurement and characterization of radioactive sources.

Quantum machine learning (QML) is an emerging field at the intersection of quantum computing and data science, exploring how quantum systems can enhance learning, optimisation, and data analysis tasks. At the same time, quantum simulation remains one of the most promising near-term applications of quantum computers, enabling the study of complex quantum systems that are intractable for classical computation. This module provides an integrated introduction to both quantum machine learning and quantum simulation. It develops an understanding of how quantum algorithms and variational methods can be applied to learning and optimisation problems, alongside the theoretical and practical frameworks required to simulate physical systems on quantum hardware. Emphasis is placed on mapping real-world problems to quantum representations, understanding algorithmic design, and evaluating performance under realistic hardware constraints.