Postdoctoral Researcher
Core
Develop computational approaches for translational cancer research, focusing on biomarker discovery, therapeutic response modeling, and scalable privacy-aware frameworks for multi-cohort studies.
Role type
Postdoctoral Researcher (Computational Oncology & AI/ML)
Builds
Multimodal foundation models, agentic AI workflows, and secure distributed analysis pipelines for cancer genomics and clinical data.
Domain
Computational Oncology / Biomedical Data Science / AI/ML
Deliverable
production ML models
Required skills
Multimodal and representation learning, foundation models, transfer learning, Python, R, PyTorch, Hugging Face, graph neural networks, transformers, agentic AI, federated learning, privacy-preserving analysis, reproducible workflow management (Snakemake, Nextflow), cloud/HPC environments.
Preferred skills
Experience with large-scale biomedical datasets (multi-omics, EHR), agentic AI systems, data harmonization, clinical data governance.
Responsibilities
Design and implement multimodal computational models integrating molecular, genomic, and clinical data; develop transfer learning strategies for therapeutic prediction; build secure, scalable AI/ML workflows for biomarker discovery; architect privacy-preserving computational infrastructure; develop agentic AI frameworks for data harmonization and integration; implement distributed and federated analysis pipelines; evaluate predictive value of molecular signatures across cancer types; integrate biomarker pipelines with clinical data warehouses; contribute to data governance and ethics protocols; collaborate with computational biologists, clinicians, and wet-lab scientists.
Seniority
Postdoctoral Fellow (Early-career IC)
