Multi-Agentic framework for novel target discovery

Participate in real-world AI-driven biomedical research: We are seeking an outstanding Bachelor/Master thesis student or HiWi for a fully-funded international (Aachen - New York) collaborative project to apply and further develop our multi-agentic AI framework for evidence-based hypothesis generation, and novel drug target discovery using single-cell and spatial omics data. The project builds on our previous work (Baker, Frommer et al., Cell 2026) with the goal of building explainable AI systems that can integrate biological knowledge, multimodal data, and scientific literature to generate novel and testable biomedical hypotheses.

Research Focus: You will contribute to the development of a new Multi-Agentic AI framework that combines: Single-cell and spatial transcriptomics, large language models and multimodal foundation models for hypothesis generation and target discovery. The project aims to move beyond black-box prediction towards explainable, mechanism-aware AI capable of supporting scientific reasoning and experimental design.

Potential topics:

• Application of a multi-agent AI systems for biomedical hypothesis generation and study design
• Improving scoring and explainability of AI-generated hypotheses
• Integrate multimodal biological data with mechanistic knowledge and foundation models
• Develop evaluation frameworks and open benchmarks for AI-assisted scientific target discovery

Desired Skills: Must have programming skills (Python). Experience with machine learning and deep learning (generative AI, causal modelling, or multimodal learning) and/or with single-cell, spatial omics, systems biology, or biomedical data analysis are advantageous.

We Offer: A highly interdisciplinary and collaborative research environment, Access to state-of-the-art single-cell and spatial omics datasets, Opportunities to work at the forefront of AI for scientific discovery, international collaborations and Competitive salary and benefits according to institutional guidelines

Apply: Please submit a CV, brief motivation letter, and relevant supporting documents to shayat@ukaachen.de

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