AddressThe goal of the programme is to do research in the area of deep generative models, e.g., diffusion, energy based, normalizing flow or transformer-based models. With a focus on the particular domain of molecules. The project will contribute to accelerate the drug discovery process, leading to more economic and effective drugs that can significantly improve the health and lifestyle of millions. The resulting methods are also expected to have an impact in materials science, e.g, by leading to more effective batteries.
Key responsibilities include working on deep learning, probabilistic modelling, deep generative modelling, and graph neural networks.
Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and networking with colleagues and students; planning and organising research resources and workshops.
Successful applicants will have or be near to completing a PhD in computer science, information engineering, statistics, chemistry or a related area, with extensive research experience and a strong publication record. Excellent mathematical and programming skills are essential, with experience in two or more of Bayesian methods, deep learning, deep generative models, reinforcement learning, graph neural networks.
