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The Osmanbeyoglu Lab at the University of Pittsburgh School of Medicine is recruiting NIH-funded postdoctoral researchers with strong backgrounds in machine learning and artificial intelligence to join a growing, interdisciplinary research program at the interface of ML, single-cell and spatial omics, and systems biology. The lab offers a supportive, intellectually rigorous environment for candidates seeking scientific independence, strong mentorship, and high-impact publications. We value clean code, reproducible research, thoughtful model design, and collaborative science, and we are deeply committed to the career development and long-term success of our trainees.
Job Responsibility:
Develop novel machine learning and deep learning methods—including graph neural networks, attention-based models, multimodal learning, and interpretable AI—to analyze large-scale single-cell and spatial transcriptomics datasets
Infer context-specific regulatory programs that drive cancer progression and end-stage disease
Rigorous algorithmic development, interpretability, and benchmarking on real, noisy, high-dimensional data
Method development and biological discovery
Work in close collaboration with experimental biologists, pathologists, and clinicians
Requirements:
Ph.D. in machine learning, AI, data science, computer science, or a related quantitative field
Strong programming skills in Python (experience with PyTorch, TensorFlow, or JAX preferred)
R a plus
Experience with deep learning architectures (e.g., GNNs, representation learning, attention models)
Comfort working in Linux/HPC environments and with large-scale datasets
Interest in applying ML to scientific problems
prior experience with biological data is not required
Strong communication skills and ability to work across disciplines