Sepinoud Azimi

Postdoctoral Researcher in Generative AI for Nanomedicine Design at Delft University of Technology (TU Delft). The project asks how AI can design the next generation of nanomedicines and explain why they work. The research focuses on the intersection of computer science , biomedical engineering , medical science , chemistry , materials science , and biology . You will work on generative AI methods such as VAEs , diffusion models , and reinforcement learning for nanoparticle formulation design . A key part of the work is coupling these models with counterfactual explanations to identify the minimal formulation changes that could alter biological outcomes. The post emphasizes transparent, explainable design for drug delivery systems, where the structure designed is not always the structure that acts in vivo. The position is embedded in a growing research team and connected to a Horizon Europe consortium, NAP4DIVE , coordinated by Åbo Akademi University , with partners including AstraZeneca and TU Eindhoven . Eligibility highlights: strong background in machine learning and generative modeling; interest in applying AI to real biomedical design problems. No specific language test waiver, scholarship, or tuition funding is mentioned. The post does not list a formal degree requirement, but it is a postdoctoral opening. Timeline: start in Summer 2026 ; deadline 2026-04-20 . The position is described as a 12-month appointment, with the research programme intended to continue long term. For application details, the post does not provide a direct portal or email. Candidates should follow the LinkedIn post and contact the announcer if needed.

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] .