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BSc Physics with Artificial Intelligence (Industrial)

University of Leeds

Institution
University of Leeds
Level
undergraduate
Subject
Physics with Artificial Intelligence (Industrial)
Duration
4 years
UCAS code
F311
Typical offer
A-level ABB, IB 18

Entry requirements

A level: AAA (specific subject requirements) BTEC: BTEC qualifications in relevant disciplines are considered in combination with A Level Physics and Mathematics. Applicants should contact the School to discuss. IB: 18 points at Higher Level to include 5 in Higher Level Physics and 5 in Higher Level Mathematics.

About this course

BSc in Physics with Artificial Intelligence (Industrial), a physics and computer science (artificial intelligence) degree examining the application of computational methods to physical systems. Core topics include Artificial Intelligence & Machine Learning and Research Methods. This four-year course includes a paid industrial placement year and a collaborative final-year research project. The second year builds practical competence through compulsory study in Foundation of AI: Machine Learning for Scientist and Physics with Artificial Intelligence Laboratory. The final year focuses on advanced investigation via the compulsory BSc Research Project and Advancing AI: Deep Learning for Scientists. Students can tailor their studies through optional modules spanning Advanced Quantum Physics, Quantum Matter, Advanced Optics with Photonics, Magnetism and Ferroic Materials, Theoretical Elementary Particle Physics, Advanced Mechanics, Molecular Simulation with Machine Learning: Theory and Practice, Star and Planet Formation, Cosmology, Physics in Schools, and Group Innovation Project.

Modules

  • Foundation of AI: Machine Learning for Scientist
  • Physics with Artificial Intelligence Laboratory
  • BSc Research Project
  • Advancing AI: Deep Learning for Scientists
  • Advanced Quantum Physics
  • Quantum Matter
  • Advanced Optics with Photonics
  • Magnetism and Ferroic Materials
  • Theoretical Elementary Particle Physics
  • Advanced Mechanics
  • Molecular Simulation with Machine Learning: Theory and Practice
  • Star and Planet Formation
  • Cosmology
  • Physics in Schools
  • Group Innovation Project