PhD in Computational Chemistry and Machine Learning for Binding Affinity Prediction at Lund University Lund University in Sweden

PhD opportunity in Computational Chemistry at Lund University in Sweden, focused on machine learning, binding affinity prediction, and drug discovery. The project is based in the Division of Computational Chemistry and the Computational Biochemistry group, working with quantum chemistry, statistical mechanics, biochemistry, medicinal chemistry, and structural biology.

The doctoral project aims to develop physics-informed machine-learning models for predicting the binding affinity of small molecules to biological macromolecules. The work starts from physics-based approaches such as MM/PBSA, free-energy perturbation, SQM 2.20, and fragment molecular orbital calculations, then uses modern ML methods to improve predictions and identify the physical information most relevant to binding.

The project involves close collaboration with Molecular AI at AstraZeneca and the Department of Pharmaceutical Biosciences at Uppsala University. The post is a full-time fixed-term doctoral employment starting 2026-09-01 or as agreed. The employment is for 4 years, with possible extension for teaching and other departmental duties (up to 20%).

Eligibility includes a second-cycle qualification and a relevant background such as Computational Chemistry, Bioinformatics, Machine Learning, Computer Science, Mathematics, Statistics, Physics, or a related discipline. Required skills include Python programming, independent research ability, strong communication skills, good English, and a collaborative mindset. Preferred experience includes machine learning for scientific problems, molecular modelling, molecular mechanics/dynamics, quantum mechanics, binding affinity prediction, HPC, workflow automation, and collaborative software development.

Applications must be written in English and include a CV, cover letter, degree/study certificates proving eligibility, and any additional documents such as transcripts, references, or recommendation letters. Apply via the Varbi portal before the deadline.

Deadline: 2026-04-26