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

Introduction

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.

Job Description

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 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.

Job Requirements

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).

Conditions of Employment

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 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. 3059 - max. 3881).
• 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 childrens 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.

About us

Eindhoven University of Technology is a leading international university within the Brainport region where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact today and in the future. TU/e is home to over 13000 students and more than 7000 staff forming a diverse and vibrant academic community.

Information

Do you recognize yourself in this profile and would you like to know more Please contact the hiring manager dr. Francesca Grisoni .

Visit our website for more information about the application process or the conditions of employment. You can also contact Sascha Sanchez HR advisor .

Curious to hear more about what its 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.

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application.
• A list of 1 to 3 selected publications (e.g. preprints thesis manuscript peer-reviewed papers etc) along with a summary of their content a description of their relevance for the scopes of the project your role in the research and the corresponding DOIs (if available). Preprints and conference papers can be included

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.

Please note

• You can apply online. We will not process applications sent by email and/or post.
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.