PhD Candidate in AI-Driven Design for Immunotherapy

Job summary

This position is for a PhD candidate focused on developing AI systems for cancer immunotherapy, specifically in designing protein minibinders that target peptide-MHC complexes. The role involves building computational infrastructure to enhance specificity in CAR-T cell constructs.

Qualifications

• A two-year master's degree or equivalent academic level.
• Strong background in deep learning or generative modeling with relevant project or research experience.
• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, JAX).
• Excellent analytical skills and comfort with open-ended research problems.
• Strong communication skills and a collaborative approach to interdisciplinary science.

Responsibilities

• Design and generate diverse protein minibinder libraries using advanced modeling techniques.
• Develop filtering pipelines to identify high-confidence candidates from large datasets.
• Create active learning strategies to optimize experimental validation processes.
• Integrate experimental data into model training and refinement loops.
• Explore new protein design methodologies as the field advances.
• Publish research findings in peer-reviewed journals and present at conferences.
• Contribute to teaching and mentoring within the lab.

Skills

• Experience with generative protein design tools and models (e.g., RFdiffusion, AlphaFold).
• Knowledge of immunology or antigen presentation concepts.
• Familiarity with protein structure and bioinformatics.
• Experience with high-performance computing workflows.

Education

• A master's degree (120 ECTS points) or equivalent academic qualification.