Department of Applied Mathematics and Theoretical Physics (DAMTP)
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Carries out research of worldclass excellence in a broad range of subjects across applied mathematics and theoretical physics
The Department of Applied Mathematics and Theoretical Physics (DAMTP) is one of two Mathematics Departments at the University of Cambridge, the other being the Department of Pure Mathematics and Mathematical Statistics (DPMMS). The two Departments together constitute the Faculty of Mathematics, and are responsible for the teaching of Mathematics and its applications within the Mathematical Tripos.
DAMTP has a 50year tradition of carrying out research of worldclass excellence in a broad range of subjects across applied mathematics and theoretical physics. Members of DAMTP have made seminal theoretical advances in the development of mathematical techniques and in the application of mathematics, combined with physical reasoning, to many different areas of science. A unique strength is the G K Batchelor Laboratory, in which fundamental experimental science is also performed. Research students have always played a crucial role in DAMTP research, working on demanding research problems under the supervision of leading mathematical scientists and, in many cases, moving on to become research leaders themselves. The current aims of DAMTP are to continue this tradition, in doing so broadening the range of subject areas studied and using new mathematical and computational techniques.
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Topics in conditional causal inference
With the growth of complex experimental designs and largescale observational data, causal questions arising in applications are now more targeted and precise. For example, one might ask if the treatment is effective at a ... 
Precision QCD and effective field theories with machine learning
The Standard Model (SM) serves as one the best descriptions of fundamental physics we have and the quest for its falsification has led to it being tested to an unprecedented degree. Despite its flawless performance, there ... 
Secular Dynamics of Selfgravitating Debris Discs
Debris discs are the leftovers from the epoch of planetary formation, akin to the Solar System's asteroid and Kuiper belts. They preserve a record of the physical processes at work throughout the system's age, not least ... 
Machine Learning in Inverse Problems  Learning Regularisation Functionals and Operator Corrections
In this thesis, we investigate properties of deep neural networks and their application to inverse problems. A successful classical approach to inverse problems is variational regularisation, combining knowledge and ...