PhD In Machine Learning for Drug Discovery in Low-Data Regimes

We invite applications for a fully funded PhD position within the LowDataML doctoral network, focusing on developing innovative machine-learning approaches for drug discovery under low-data conditions. LowDataML aims to bridge the gap between current ML/AI tools — which typically require large, dense datasets — and the realities of lab-scale chemistry and early-stage drug research, where data are often scarce, sparse or incomplete.

Information
Your tasks will include:

• Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning — for prediction of molecular properties, activity, or synthetic feasibility.
• Working at the interface of cheminformatics, synthetic chemistry and drug discovery, collaborating with partners across academia and industry.
• Contributing to accelerating the discovery of new therapeutics with machine learning.
• Communicating the results of your research through publications in scientific journals and presentations at conferences.

You will work at the interface between AI, chemistry, and biology, with a proactive and interdisciplinary attitude. You will become a member of the Molecular Machine Learning team (led by Prof. F. Grisoni), whose mission is to augment human intelligence in drug discovery with novel AI technology. You will also be embedded in the Chemical Biology group (led by Prof. L. Brunsveld), the Dept. of Biomedical Engineering, the Institute for Complex Molecular Systems, and the Eindhoven AI Systems Institute, which are characterized by a highly interdisciplinary and collaborative approach to science and research.

The Department of Biomedical Engineering offers top-level education and research in one of the most relevant and exciting scientific disciplines of the 21st century: engineering health. In combining engineering and life sciences, through challenge-based learning and a multidisciplinary approach in collaboration with hospitals, industry and others, the department addresses the great challenges of the future, striving to improve healthcare and society as a whole.

Background:

• An MSc degree (or equivalent) in Chemistry, Medicinal Chemistry, Chemical Engineering, Cheminformatics, Bioinformatics, Computer Science, or a related discipline.
• Foundational understanding of organic chemistry, molecular structure, and/or drug-discovery principles.
• Demonstrated interest in applying machine learning or computational methods to chemical or biological problems.
• Motivation to work with low-data, sparse, or noisy datasets, typical in early-stage drug discovery.

Technical skills:

• Proficiency in Python (required).
• Practical experience with machine learning or deep learning workflows (required).
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or scikit-learn (required).
• Experience with cheminformatics tools such as RDKit (required or strongly desirable).
• Basic knowledge of data handling, version control (e.g., Git), and reproducible scientific programming (desirable).
• Understanding of molecular representations (e.g., fingerprints, SMILES, graphs) and/or computational chemistry concepts (desirable).
• Familiarity with chemical or biological databases (e.g., ChEMBL, PubChem, PDB) is a plus.
• Experience with Bayesian modelling, transfer learning, few-shot learning, or other data-efficient ML methods is advantageous but not required.

Soft skills:

• A research oriented and quantitative thinking attitude.
• Proven ability to work in interdisciplinary teams.
• Good writing and presentation skills.
• Ability and willingness to collaborate in an international, interdisciplinary work environment.
• Fluent in spoken and written English (C1 level).

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 - max. € 3,881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager dr. Francesca Grisoni, f.grisoni@tue.nl.

Visit our website for more information about the application process or the conditions of employment. You can also contact Sascha Sanchez, HR advisor, s.j.m.g.sanchez.van.oort@tue.nl.

Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.