Are you ready to take the next step in your career and make a real impact in the field of data-driven life science? We are offering an exciting opportunity as an Industrial PhD student to work on a project that applies AI and molecular dynamics simulations to discover small molecule binders to difficult-to-drug proteins. This project is a collaboration between AstraZeneca (AZ), Stockholm University (SU) & Chalmers University of Technology (CTH), financed by Data-Driven Life Science (DDLS) program. The successful candidate will also be part of the DDLS Research School and a PhD student at SU. You will be supported by Marco Klähn, Ola Engkvist & Werngard Czechtizky at AZ as well as receive academic mentorship and guidance from Erik Lindahl (Professor) at SU and Rocío Mercado (Assistant Professor) at CTH. The position will be based at AstraZeneca in Gothenburg, Sweden. Accountabilities As part of this role, you will performing molecular dynamics (MD) simulations to understand protein flexibility. Based on the trajectories from the MD simulations you will use deep learning technologies like diffusion models to model the protein flexibility. You will use the deep learning model describing the protein flexibility to identify druggable pockets on the protein surface and with the generative AI tool REINVENT design potential binders to the pockets. You will also summarize findings in manuscripts to be presented at high impact international conferences and scientific journals. Essential Requirements * Masters degree in a subject relevant to the project (e.g., Chemistry, Biophysics, Biotechnology, Data Science & AI, etc) * Ability to work in a Linux environment * Experience in PyTorch or similar deep learning framework To be successful in this role, it is of key importance to demonstrate a high level of independence in the pursuit of your work. You need to have excellent collaborative skills, a highly professional approach and also well-developed abilities to analyse and work with complex issues. Desired Qualifications * Knowledge of statistical mechanics * Knowledge of data science including machine learning * Experience of application of deep learning in a life science context * Experience of GROMACS or similar molecular dynamics tool * Strong programming skills, preferably in Python So what's next? We welcome your application no later than June 10 2024. We will review applications on a regular basis so please apply as soon as possible. Are you ready to join a team that's pushing the boundaries of science to deliver life-changing medicines? If your passion is science and you want to be part of a team that makes a bigger impact on patients' lives, then there's no better place to be. Apply today!