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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational
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text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference
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The Role: We are seeking an outstanding computational biologist to join the BioFAIR Pathfinder project to develop computational framework for gene regulatory network (GRN) inference methods in
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Fellowship in Computer Science in Amin lab Prof. Nada Amin is looking for a postdoctoral fellow, who will be mentored by her at Harvard SEAS. The goal is to develop new training and inference approaches
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scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Enable real-time adaptive learning
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assimilation, both in the context of meterology and elsewhere Read, understand, and summarize the latest literature on inference in state-space models with particle filters Devise, implement, and inspect
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-shot reasoning and scene-graph inference. Ensure the system is deployment-ready by supporting benchmarking of inference speed, compute efficiency, and scalability with concurrent agents. Maintain high
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-conditioned inference. This position has an anticipated start date of October 1, 2026 We’re here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role
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objective is to create a new generation of algorithms for inference and decision-making by pushing the boundaries of the underlying computational techniques. SURE-AI researchers will drive a transformative
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for spatial population genetics. Our research integrates custom neural architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will