Doctorate on artificial intelligence for drug discovery

Research / Academic


Artificial intelligence (AI) has the potential to revolutionize drug discovery. By analyzing data sets containing biological and chemical information, AI algorithms can identify patterns and relationships that may not be obvious to human researchers. AI can be used to help design new drugs by predicting the likely biological activity of potential compounds. This can save time and resources by allowing researchers to focus on the most promising candidates. In addition, AI can be used to study the complex interactions between drugs and biological systems, leading to a better understanding of how drugs work and how they can be improved.

Did you guess that the above text was automatically generated with AI1)? No? This shows what deep learning can do. But while the “artificial intelligence revolution” is transforming natural language processing, its potential in drug discovery remains untapped. Certainly, deep learning has accelerated tasks like synthesis prediction and molecular design, but we still lack methods to efficiently map the vast “chemical universe” and efficiently discover drugs for old and new diseases. These aspects provide an exciting opportunity to rethink current approaches to AI-driven drug discovery.

1) See learn more!

This PhD position is part of an ERC-funded project that aims to advance the potential of AI for drug discovery by providing innovative ways to represent – and learn – molecular information with AI. The project is driven by methodological innovation and aims to discover novel bioactive molecules faster. Additionally, the novel approaches you will develop will be applied prospectively in the wet lab, providing a unique opportunity to validate the AI ​​predictions in a real-world environment.

Your tasks include:

  • Development and implementation of innovative algorithms for capturing sophisticated chemical information with AI for drug discovery.
  • Implement cutting-edge deep learning approaches to efficiently learn from small data regimes.
  • Collaborate and interact with researchers in medicinal chemistry and chemical biology to gain a deeper understanding of the underlying mechanisms and for experimental validation.
  • Communicate the results of your research through publications in scientific journals and presentations at conferences.
  • Mentoring and mentoring of master and bachelor students working on related projects.

Her work moves at the interface between AI, chemistry and biology and is driven by creative and interdisciplinary thinking. You become a member of Molecular Machine Learning Team (led by Dr. F. Grisoni) whose mission is to augment human intelligence in drug discovery through innovations in AI. They are embedded in the Chemical Biology working group, the Department of Biomedical Engineering and the Institute for Complex Molecular Systems, which are characterized by a highly interdisciplinary and collaborative scientific approach.



  • A master’s degree (or equivalent university degree) in chemistry, chemical engineering, biomedical engineering, biochemistry, molecular biology or related disciplines.
  • Good understanding of (and affinity with) statistics and mathematics.

technical skills:

  • Advanced Python skills (required).
  • Familiarity with Unix/Unix-like operating systems (desirable).
  • Familiarity with machine learning concepts (required).
  • Knowledge of deep learning frameworks such as Tensorflow or PyTorch (desirable).

soft skills:

  • A research-oriented and quantitative mindset.
  • Ability to work in a team and interest in interdisciplinary science.
  • Good writing and presentation skills and motivation to improve them.
  • Fluent written and spoken English (C1 level).

Salary Benefits:

A meaningful job at a dynamic and up-and-coming university, in an interdisciplinary environment and in an international network. You work on a beautiful, green campus within walking distance of the main train station. In addition, we offer you:

  • Full-time employment for four years, with an interim evaluation (go/no-go) after nine months. You spend 10% of your employment on teaching tasks.
  • Salary and benefits (e.g. pension scheme, paid pregnancy and maternity leave, partially paid parental leave) acc collective agreement for Dutch universities, scale P (min. €2,541,- max. €3,247,-).
  • A year-end bonus of 8.3% and annual holiday pay of 8%.
  • Quality training programs and other support to grow into a confident, autonomous scientific researcher. At the TU/e ​​​​we challenge you to take your learning process into your own hands.
  • An excellent technical infrastructure, day-care centers and sports facilities on campus.
  • Subsidy for travel expenses, home work and internet costs.
  • A staff immigration team and a tax equalization scheme (the 30% rule) for international candidates.

working hours:

38 hours a week


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