PhD in Machine Learning for Drug Discovery in Low-Data Regimes Eindhoven University of Technology in Netherlands

This fully funded PhD position at Eindhoven University of Technology is part of the LowDataML doctoral network, focusing on the development of innovative machine learning approaches for drug discovery in low-data regimes. The project aims to bridge the gap between current ML/AI tools, which typically require large datasets, and the realities of lab-scale chemistry and early-stage drug research, where data are often scarce or incomplete. As a PhD candidate, you will develop and benchmark ML/AI algorithms such as few-shot learning, transfer learning, and data-efficient representation learning for predicting molecular properties, activity, and synthetic feasibility. You will work at the interface of cheminformatics, synthetic chemistry, and drug discovery, collaborating with academic and industry partners to accelerate the discovery of new therapeutics using machine learning.

You will join the Molecular Machine Learning team led by Dr. Francesca Grisoni, whose mission is to augment human intelligence in drug discovery with novel AI technology. The position is embedded in the Chemical Biology group led by Prof. Luc Brunsveld, within the Department of Biomedical Engineering, the Institute for Complex Molecular Systems, and the Eindhoven AI Systems Institute. These groups offer a highly interdisciplinary and collaborative research environment, combining engineering and life sciences to address major healthcare challenges.

The position offers full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your time on teaching tasks. The salary is in accordance with the Collective Labour Agreement for Dutch Universities, scale P (€3,059 - €3,881 per month), with additional benefits including a year-end bonus, vacation pay, pension scheme, paid pregnancy and maternity leave, partially paid parental leave, commuting and home working allowance, and a tax compensation scheme for international candidates. High-quality training programs and support for personal and professional development are provided, along with excellent technical infrastructure and campus facilities.

Applicants must hold an MSc degree (or equivalent) in Chemistry, Medicinal Chemistry, Chemical Engineering, Cheminformatics, Bioinformatics, Computer Science, or a related discipline. Required skills include proficiency in Python, experience with machine learning or deep learning workflows, familiarity with ML frameworks (PyTorch, TensorFlow, scikit-learn), and cheminformatics tools (RDKit). Desirable skills include data handling, version control, reproducible scientific programming, molecular representations, computational chemistry concepts, and familiarity with chemical or biological databases. Experience with Bayesian modelling, transfer learning, few-shot learning, or other data-efficient ML methods is advantageous. Candidates should have a research-oriented and quantitative thinking attitude, proven ability to work in interdisciplinary teams, good writing and presentation skills, and fluency in English (C1 level).

To apply, submit a complete application online including a cover letter, CV with publications and references, and a list of selected publications with summaries and DOIs if available. The vacancy will remain open until filled, with a final application deadline of January 4, 2026. For further information, contact Dr. Francesca Grisoni ([email protected]) or HR advisor Sascha Sanchez ([email protected]). Applications sent by email or post will not be processed.