Information about the research and the project

This call is the second of two from the Data Science and AI division at CSE to recruit a PhD student for a project on Machine Learning for the Natural Sciences.

Machine learning provides new exciting new opportunities in the natural sciences such as physics, chemistry, and biology. Its potential application areas span from designing new drugs against multi-resistant pathogens and understanding the impact of gene defects on a protein’s function to speeding up computer simulations to understand fundamental scientific phenomena and design optimal algorithms for near-term quantum computers. The path towards these applications can leverage the power of deep learning to represent and process high-dimensional data in an effective manner and encode natural laws and symmetries.

The Artificial Intelligence and Machine Learning for the Natural Sciences (AIMLeNS) group (head: Simon Olsson) is an interdisciplinary group focusing on problems in the natural sciences and the development of machine learning and AI systems towards these problems. The recruited PhD student will integrate into the AIMLeNS group.

This project will develop methods to sample 3D configurations of protein molecules using deep generative models and related techniques. The project will develop the necessary theory to integrate physical symmetries into deep generative models and train them using experimental data and physical models.

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