If you are someone who loves uncovering how the world works and you are excited by the possibilities of new technology, studying Physics with Artificial Intelligence at the University of Leeds could be an inspiring next step. This degree allows you to explore the fundamental laws of nature while learning how modern AI techniques can model complex systems, analyse data, and tackle scientific challenges that were once out of reach.
Our Physics programmes emphasise how interconnected the subject is, both in teaching and assessment. In years 1 and 2, through in‑course assessments, you will demonstrate a solid understanding of the material needed to progress each year. End‑of‑year exams and a portfolio of transferable skills will give you the chance to show your full mastery of the subject.
In your first year, you will study core topics in physics and mathematics, including mechanics, astrophysics, thermal physics and thermodynamics, complex numbers and differential equations, waves, vibrations and optics, an introduction to quantum physics, functions and calculus, special relativity, and the physics of solids. Alongside this, every student takes part in the undergraduate laboratory, where you will develop essential practical skills. Coding is a central part of the Physics with AI degree, and you will begin building these skills from your first semester. In semester two, you will take Artificial Intelligence for Scientists, a module introducing the key concepts of AI, its ethical and legal implications, and its applications across scientific disciplines.
Your second year follows a similar structure. You will continue with the core physics material common to all our programmes, again demonstrating threshold understanding through in‑course assessments before completing exams, a transferable‑skills portfolio, and a coding project. Topics include quantum mechanics, electromagnetism, linear algebra and vector calculus, statistical physics, and condensed‑matter physics. You will also take two modules specific to your programme: Foundations of AI: Machine Learning for Scientists, which explores the principles behind machine‑learning algorithms and their use in modern scientific problems, and a dedicated laboratory course designed to bridge your AI and coding skills with experimental work.
The final year of the BSc programme centres on a substantial 40‑credit project, where you will bring together everything you have learned in physics, mathematics, coding, and artificial intelligence to carry out an independent investigation a under the guidance of one of our world‑leading academics You will also study AI deep‑learning methods, building your understanding of neural networks and their applications. At this stage, the full breadth of the programme opens up, allowing you to choose from a wide range of specialist modules offered by the School, as well as selected options from related disciplines.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS1000 | First Year Physics Assessment | 100 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1010 | Mechanics, Relativity and Astrophysics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1020 | Thermodynamics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1030 | Electronics, Solid State and Introduction to Quantum Physics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1040 | Vibrations, Waves and Optics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1050 | Coding and Experimental Physics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS1700 | Artificial Intelligence for Scientists | 10 | Semester 2 (Jan to Jun) |
Candidates can study up to 10 credits from the following optional modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS1500 | Introduction to Nanotechnology | 10 | Semester 1 (Sep to Jan) | |
| PHAS1510 | Planets and the Search for Life | 10 | Semester 1 (Sep to Jan) |
Candidates may alternatively study 10 credits of discovery modules.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS2000 | 2nd year Physics Assessment | 80 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS2010 | Quantum Mechanics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS2020 | Statistical Mechanics and Computation | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS2030 | Condensed Matter Physics | 0 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS2040 | Electromagnetism | 0 | Semesters 1 & 2 (Sep to Jun) |
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS3000 | Project | 40 | Semesters 1 & 2 (Sep to Jun) |
Candidates will be required to study 60 credits from the following lists of optional modules:
Candidates can choose to study up to 60 credits from list A:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS3100 | Star and Planet Formation | 20 | Semester 1 (Sep to Jan) | |
| PHAS3110 | Cosmology | 20 | Semester 2 (Jan to Jun) | |
| PHAS3200 | Advanced Optics with Photonics | 20 | Semester 1 (Sep to Jan) | |
| PHAS3300 | Quantum Matter | 20 | Semester 1 (Sep to Jan) | |
| PHAS3310 | Magnetism and Ferroic Materials | 20 | Semester 2 (Jan to Jun) | |
| PHAS3400 | Advanced Quantum Physics | 20 | Semester 1 (Sep to Jan) | |
| PHAS3410 | Theoretical Elementary Particle Physics | 20 | Semester 2 (Jan to Jun) | |
| PHAS3420 | Advanced Mechanics | 20 | Semester 1 (Sep to Jan) | |
| PHAS3500 | The Physics of the Molecules of Life | 20 | Semester 1 (Sep to Jan) | |
| PHAS3510 | Molecular Simulation with Machine Learning: Theory and Practice | 20 | Semesters 1 & 2 (Sep to Jun) |
Candidates can choose to study up to 20 credits from list B:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| PHAS3600 | Physics in Schools | 20 | Semesters 1 & 2 (Sep to Jun) | |
| PHAS3610 | Group Innovation Project | 20 | Semesters 1 & 2 (Sep to Jun) |
Candidates can choose to study up to 20 credits from list C:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| CAPE3331 | Nuclear Operations | 20 | Semesters 1 & 2 (Sep to Jun) | |
| MEDP3512 | Magnetic Resonance Imaging | 10 | Semester 1 (Sep to Jan) | |
| MEDP3514 | Ultrasound Imaging | 10 | Semester 2 (Jan to Jun) | |
| MEDP3531 | Medical X-ray imaging | 10 | Semester 1 (Sep to Jan) | |
| MEDP3532 | X-ray Computed Tomography | 10 | Semester 2 (Jan to Jun) | |
| PHIL3852 | Philosophy of Modern Physics | 20 | Semester 1 (Sep to Jan) | |
| SOEE3151 | Dynamics of Weather Systems | 10 | Semester 2 (Jan to Jun) | |
| SOEE3410 | Atmosphere and Ocean Climate Change Processes | 10 | Semester 1 (Sep to Jan) | |
| SOEE3535 | Atmospheric Physics | 10 | Semester 2 (Jan to Jun) | |
| SOEE3610 | Oceanography in the Earth System | 10 | Semester 1 (Sep to Jan) |
Last updated: 12/05/2026 16:36:54
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