MPhys (Hons) Physics with Artificial Intelligence (Industrial) Integrated Master's degree at the University of Leeds

Learn how to describe motion through physical space, together with the general causes of that motion: forces and energies. Develop mathematical skills to describe mechanical processes. Cover special relativity and Einstein's theories. Learn how to apply basic physical principles to objects in the Universe and explore the basics of radiation.

Explore the underpinning theories and concepts of thermodynamics. Examples and applications will be used to allow you to build your understanding and application of this branch of physics, including in sustainable energy.

Cover the underpinning theories and concepts including mechanics of solids, Bohr atom, atomic electron states, elementary bonding, elasticity, Photoelectric effect, Compton scattering, De Broglie relation, Wave-particle duality, Crystal structure and X-ray diffraction. Analyse and design simple electric circuits. Learn the principles of Boolean algebra and its application in digital logic design.

Learn about oscillators, energy and resonance, different types of waves, energy/power transfer, reflection and transmission, impedance, superposition and interference, the wave-like behaviour of light, mirrors, lenses, nonlinear optics and lasers, the solution of 2nd order partial differential equations, complex numbers, Fourier series and an introduction to Fourier transforms.

Develop practical experimental, computational, communication and employability skills. Build experimental skills through a range of laboratory tasks undertaken throughout the year and be introduced to programming using the Python computer programming language. Undertake tasks and assessments designed to improve your teamwork and presentation skills, as well as reflective practice.

This module introduces key concepts in artificial intelligence (AI), exploring the differences between narrow AI, which excels at specific tasks, and general AI, which aims to replicate human-like cognitive abilities. Through real-world examples from scientific applications, you'll gain insight into the various classes of AI systems, such as expert systems, neural networks, and reinforcement learning models. The module emphasises how AI is transforming scientific fields, including physics and ch

The smallest possible devices that can be fabricated are on the nanometre length scale. Miniaturisation of devices offers many new technological opportunities. The physical properties of nanomaterials differ from both the constituent atoms and the bulk material. This module aims to introduce the physics behind nanotechnology in a semi-quantitative manner, without requiring knowledge of quantum mechanics or Maxwell's equations. Describe the physics of atoms and molecules, before moving on to disc

Explore the multitude of planets that are currently being discovered around other stars and compare them to those in our solar system. This module will concentrate on the concepts involved and is non-mathematical, and therefore amenable to students of the arts, humanities and sciences. Examine the origin and evolution of the solar system and how it is likely to have produced the range of planets, moons and minor bodies that we see today. Contrasted with the range of extra-solar planets, their de

Learn how to describe quantum systems using wavefunctions, operators and linear algebra and how to predict outcomes of measurements on quantum systems. Solve the Schrodinger equation for simple model systems and understand the structure of atoms and molecules using the exclusion principle and spin. Learn about the structure of the atomic nucleus, predict various forms of radioactive decay and nuclear reactions, describe scattering processes between elementary particles and understand the key com

Explore the concepts and applications of statistical mechanics, which are key to understanding the behaviour of small-particle systems. This module will also enable you to translate descriptions of physical problems and data analysis processes into short programs to read and manipulate data, analyse and present the results for problems relevant to physics using a programming language.

Learn about the use of the density of states to explain some of the differences between metals, semiconductors and insulators. Cover how to derive the free-electron density of states, perform straight-forward calculations based on the free-electron theory and how a periodic potential modifies the free-electron dispersion relation, solving problems on the transport properties of semiconductors, and calculating the magnetic properties of paramagnets and ferromagnets. Build skills in communicating

Learn how to use the integral versions of Maxwell's equations and to calculate fields in cases of simple symmetric geometry, calculate the force and energy in electric and magnetic fields, Maxwell's equations in both integral and differential form and discuss their derivation from the physical laws of electromagnetism. Analyse simple AC circuits containing resistors, capacitors and inductors and apply logic principles to real-world scenarios in electronics and emerging technologies, developing t

We'll introduce basic techniques from statistical machine learning for classification and regression using Python. Throughout the module, you may cover areas like loss functions, optimisation, gradient decent, linear regression, logistic regression, support vector machines, decision trees, Bayesian learning and basic neural networks. You could also learn how to assess the error of a fitted model and explain the fitting algorithm. You'll also use software packages to perform classification and re

This module further develops key computational experimental and research skills. This includes understanding the appropriate use of experimental and measuring equipment and computational techniques, being able to draw conclusions from results obtained as well as understand the accuracy of those results to critically analyse the obtained data, as well as presenting those results in an appropriate fashion for different audiences.

The module introduces the field of deep learning, giving you the practical skills and expertise to use neural networks to solve problems in science and engineering. Throughout this module, you may cover areas such as neural networks to solve problems in science and engineering.