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Physics and computer science are two sides of the same scientific coin. Physics lays the foundation with its fundamental laws, while computer science translates those laws into simulations, modelling everything from brain activity to the vastness of galaxies. This synergy creates a new frontier: computational physics. Here, computers become powerful tools to tackle complex problems in all aspects of modern physics.
The University of Nottingham’s School of Physics and Astronomy recognises this connection and offers programmes that equip students with the skills needed to thrive in these rapidly growing fields. It has 98% of its research classified as “world-leading” (4*) or “internationally excellent” (3*) in the Research Excellence Framework 2021. This translates to a consistent ranking that makes it second only to the University of Cambridge in research quality over the past three national research assessments. Leading the charge in topics ranging from computational astrophysics to non-equilibrium quantum dynamics are highly respected figures like Professor Juan Garrahan, Dr. Maggie Lieu, Professor Jamie Bolton, Professor Simon Dye, and Dr. Adam Smith.
The inaugural intake for the four-year bachelor’s (BSc) and five-year integrated master’s (MSci) in Physics with Computer Science programmes opened for applications in June 2024, offering a brand-new opportunity for students seeking a cutting-edge education.
Both programmes carefully integrate computing modules throughout the curriculum. In the first year, students build a strong foundation in core physics alongside an introductory course in “Computing for Physical Scientists”. The second year builds upon this base, teaching students how to apply computing techniques in the context of experimental physics. The third year, spent within the School of Computer Science, provides a concentrated dose of relevant skills. Sample modules include “Databases, Interfaces and Software Design Principles,” “Introduction to Software Engineering,” and the ever-important “Fundamentals of Artificial Intelligence.”
The Sherwood Simluations hydrodynamical simulations of the intergalactic medium.
University of Nottingham students have opportunities to contribute to advanced research projects in computational physics, including Artificial Intelligence and Machine Learning. Source: Professor James Bolton, University of Nottingham
Upon returning to the School of Physics and Astronomy in the fourth year, students delve deeper into “Scientific Computing” and embark on a research project in computational physics. Those who choose to stay for the final MSci year will benefit from bespoke modules that focus on building professional skills, such as “Imaging and Image Processing”. All these skills can then be applied to a major 50 credit research project working on front-line research in computational physics. Recent students have worked on exciting topics such as “Simulating the Universe using Machine Learning” and “A Quantum Enabled Brain Computer Interface”.
The School also offers a one-year postgraduate MSc in Machine Learning in Science (MLiS) programme that focuses on applying machine learning and artificial intelligence techniques to real-world problems. Students gain practical experience through a 60-credit research project completed alongside taught modules. This project allows them to write a dissertation under the supervision of academic staff, with the possibility of collaborating with external partners and industry professionals. Applications are encouraged from enthusiastic students with a previous undergraduate degree (or equivalent) in physics, mathematics, computer science, chemistry, or engineering.
Whichever programme they join, career-readiness is guaranteed. Graduates from the BSc/MSci Physics with Computer Science programmes and the MSc in Machine Learning in Science have great career prospects. The school’s Careers and Placement Officer works tirelessly to connect students with industry opportunities. The School’s membership in the White Rose Industrial Physics Academy further enhances these efforts, opening doors to internships, placements, and graduate employment. Career talks, company visits, and workshops provide students with more insights and networking necessary to navigate the job market and secure fulfilling careers.
With such resources available, MSc Machine Learning in Science graduates have gone on to thrive in industries such as finance, software engineering and data science, reporting an average starting salary of 34,063 pounds (HESA Graduate Outcomes 2020). Others pursue PhDs, while some return to previous employers with newfound skills. Graduates like Elliot Hicks, a 2023 MSc Machine Learning in Science graduate, exemplify the programme’s impact.
“The MLiS course at Nottingham was the best time I’ve had in academia — the content, staff and projects were fantastic,” he says. “Crucially, my work with an external company was my first piece of work experience in the field of machine learning and it gave me a huge advantage when applying for positions after I graduated.”
The silver magnetic field-shaping system shown in the foreground was designed using AI methods and commercialised with an industry partner Magnetic Shields Ltd. 91% of undergraduates from the Schools of Physics and Astronomy and Computer Science secured employment or pursued further study within 15 months of graduation, with an average annual salary of 34,382 pounds (HESA Graduate Outcomes 2020-2021). Source: University of Nottingham
Those seeking a future career in research or academia would do well to make the most of the school’s world-leading research groups in diverse areas like Astronomy, Cold Atoms & Quantum Optics, Condensed Matter Theory, Experimental Condensed Matter & Nanoscience, Magnetic Resonance Imaging & Spectroscopy, and Particle Cosmology. This allows students to contribute to groundbreaking discoveries while honing their research skills. Just ask Jonah Powley, who undertook a project on N-body simulation of the solar system as a third-year undergraduate student.
“My scientific computing project in N-body simulations increased my confidence in utilising advanced computational techniques, such as vectorisation and signal processing, and has already helped me secure an (Astro)physics summer studentship as well as a master in scientific computing,” he says.
Whichever programme they choose, all students benefit from the school’s supportive culture. There is a Wellbeing Team and access to wider university resources to ensure students have the tools to thrive both academically and personally. The student-run Physics Society (PhysSoc) organises social and academic events, creating a strong sense of community. Recently refurbished common rooms make the learning environment more comfortable, welcoming and collaborative.
Learn more about the School of Physics & Astronomy
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