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, chromatin profiling, genomics, spatial transcriptomics and single-cell data. Apply statistical, machine learning, and network-based approaches to analyze high-dimensional biological data. Collaborate closely
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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by applying existing and novel computational biology, bioinformatic, and machine learning algorithms to sequencing datasets and correlating them with multi-dimensional clinical datasets that contain
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: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI
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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience