Sepinoud Azimi
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5 months ago
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Postdoc Researcher in Generative AI for Nanomedicine Design Delft University of Technology in Netherlands
Degree Level
Postdoc
Field of study
Computer Science
Funding
One-year postdoc position at TU Delft starting summer 2026. The post is part of NAP4DIVE (Horizon Europe) and is connected to an active publication pipeline on AI methods in nanomedicine. No stipend amount is stated.
Deadline
Expired
Country
Netherlands
University
Delft University of Technology

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About this position
Tu Delft is advertising a postdoc in Generative AI for Nanomedicine Design within the Technology, Policy and Management context. The project focuses on computational research on nanoparticle design for drug delivery and is part of NAP4DIVE (Horizon Europe), with work feeding into an active publication pipeline on AI methods in nanomedicine.
The ideal candidate has a strong machine learning background and can work confidently with deep learning, generative AI, diffusion models, variational autoencoders (VAEs), transformers for molecular design, and graph neural networks. The post specifically values experience applying these methods to real scientific or engineering problems, not only benchmark datasets.
Research interests that fit especially well include nanoparticle design, molecular property prediction, protein-ligand interaction modeling, pharmacology ML, materials informatics, computational chemistry, graph-based generative models, and SMILES-based generation.
This is a one-year position starting in summer 2026. The post does not mention a stipend amount, but it is a funded research postdoc role. The deadline is 2026-04-20.
To apply, use the application link provided in the post. For questions, contact Sepinoud Azimi at [email protected].
Funding details
One-year postdoc position at TU Delft starting summer 2026. The post is part of NAP4DIVE (Horizon Europe) and is connected to an active publication pipeline on AI methods in nanomedicine. No stipend amount is stated.
What's required
A strong machine learning background is essential. The candidate should be fluent in modern machine learning, deep learning, generative methods such as diffusion models, VAEs, and transformers for molecular design, and graph neural networks. Experience applying these methods to real scientific or engineering problems is required, not only benchmark datasets. Prior work at the interface of ML and bio-chemical systems is expected; nanoparticle experience is ideal, and adjacent experience in molecular property prediction, protein-ligand interaction modeling, pharmacology ML, materials informatics, computational chemistry, diffusion models for molecules, graph-based generative models, or SMILES-based generation is highly valued.
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