PhD Position (Doctoral Candidate 10) in the Marie Skłodowska-Curie Doctoral Network ANA-KIN

Universiteit Leiden; Leiden; Vor Ort; EUR 33.000 - 40.000; Vollzeit

Zusammenfassung

Universiteit Leiden invites applications for a PhD position (Doctoral Candidate 10) within the ANA-KIN MSCA Doctoral Network. The project focuses on deep-learning-enabled kinase drug discovery and interdisciplinary collaboration across Europe and the USA.

You will develop AI-driven, multi-modal models and contribute to consortium-wide training and secondments. You will work under renowned supervision at Leiden University, with secondments at partner organizations in Belgium and across the

Qualifikationen

• Master’s degree in a relevant field is required.
• Experience with deep learning or AI in chemical biology is desirable.
• Strong computational and data-analysis skills.

Aufgaben

• Conduct original research in deep learning and cheminformatics for kinase drug discovery.
• Develop and share models and decision-support dashboards following FAIR and open-science principles.
• Collaborate closely with experimental and computational DCs across the ANA-KIN network.
• Publish and present scientific articles at international journals and conferences.
• Participate in ANA-KIN network-wide training events, schools and outreach activities.
• Contribute to educational activities.
• Write a dissertation.

Kenntnisse

Deep learning

AI

Cheminformatics

Ausbildung

MSc degree in Computer Science, AI, (Computational) Chemistry, Bioinformatics, Biopharmaceutical Sciences, or a relevant related field

Jobbeschreibung

PhD Position (Doctoral Candidate 10) in the Marie Skłodowska-Curie Doctoral Network ANA-KIN Job Description PhD Position (Doctoral Candidate 10) in the Marie Skłodowska-Curie Doctoral Network ANA-KIN Vacancy number: 16967 Job type: PhD positions Contract hours min: 38 Contract hours max: 38 Location: Leiden Applying is possible up to and including: 12 November 2026

The Faculty of Science and Leiden Academic Centre for Drug Research (LACDR) are looking for candidates for a PhD Position (Doctoral Candidate 10) in the Marie Skłodowska-Curie Doctoral Network ANA-KIN

ANA-KIN is a Marie Skłodowska-Curie Actions (MSCA) Doctoral Network funded by the European Union’s Horizon Europe programme. Over the past two decades, small-molecule kinase inhibitors have transformed cancer therapy, yet nearly all of them target the highly conserved ATP-binding site, which limits selectivity and allows resistance to emerge. ANA-KIN aims to train a new generation of interdisciplinary researchers capable of redefining kinase drug discovery beyond the ATP paradigm, by developing the experimental and computational tools, modular assays and workflows needed to systematically target non-conserved regulatory interfaces, such as kinase dimerization and substrate-binding sites. Proof-of-concept will be demonstrated on three representative kinases: EGFR, BRAF and the understudied “dark” kinase LTK.

ANA-KIN brings together 11 beneficiaries and 9 associated partners across Europe and the USA, including Aristotle University of Thessaloniki (coordinator), University of Crete, Goethe University Frankfurt, Saarland University, EPFL, CNRS, Pompeu Fabra University, VIB, KU Leuven, INSERM and Leiden University, together with industrial partners such as AstraZeneca, Novartis, Servier and Skylight Therapeutics. The network combines chemical biology, structural biophysics (single-molecule FRET, cryo-EM, nanodiscs), AI-driven drug design and cell biology in an integrated learn‒design‒test loop. Twelve Doctoral Candidates (DCs) will each pursue an individual research project, benefit from co-supervision across institutions and sectors, carry out two research secondments at partner organizations, and follow a structured network-wide training program covering scientific, transferable, open-science and entrepreneurial skills.

In this PhD project (DC10, Work Package 3 ‒ Discovery), you will develop deep-learning architectures that integrate text-mined, cheminformatic, and biophysical descriptors to predict interface binding, allosteric signatures and resistance liabilities of kinase ligands. The models will learn jointly across peptide and small-molecule modalities and will be used to generate hybrid peptidomimetic design hypotheses. You will translate these models into consortium-wide prioritisation dashboards that support hit triage and analogue selection in the discovery campaigns of ANA-KIN, and work in close reciprocal feedback with DC9 (Saarland University) on structure-based and machine-learning pipelines. Model predictions will be validated prospectively with the experimental partners in the network.

The expected results are a resistance-aware AI workflow for non-ATP interface ligands, accompanied by public models, datasets and interpretable feature maps, and prospective design case studies demonstrating improved optimisation trajectories. To reinforce structural grounding and experimental validation, you will carry out two secondments: at VIB (Belgium, Prof. Ruslan Efremov; 4 months) to extract structural classes and conformational features from cryo-EM data and use them to refine model constraints, and at Skylight Therapeutics (Belgium, Dr. Joshua Salafsky; 3 months) to test model predictions using second-harmonic generation (SHG)-based conformational profiling. The project will be supervised by Prof. Gerard J.P. van Westen (Leiden University), with Prof. Ruslan Efremov and Dr. Joshua Salafsky as co-supervisors.

What you will do

• Conduct original and novel research in deep learning, AI, and cheminformatics for kinase drug discovery;
• Develop, benchmark and openly share models, datasets and decision-support dashboards following FAIR and open-science principles;
• Collaborate closely with experimental and computational DCs in the network, and carry out two research secondments abroad (VIB and Skylight Therapeutics, Belgium);
• Publish and present scientific articles at international journals and conferences;
• Participate in the ANA-KIN network-wide training events, schools and outreach activities;
• Contribute to educational activities;
• Write a dissertation.

What you bring

• A MSc degree in Computer Science, AI, (Computational) Chemistry, Bioinformatics, Biopharmaceutical Sciences, or a relevant related field;