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Field
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-of-the-art machine learning/deep learning models for RNA sequence design, structure prediction, and property optimization. Process, curate, and model high-throughput screening and genomics datasets
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publications. Requirements: PhD in Computer Science, AI, Data Science, Materials Science/Engineering, Chemical/Mechanical Engineering or a related field. Strong machine-learning/data-science or computational
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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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Supervise and mentor research associates, engineers, and PhD students working on the project Collaborate with perception, controls, systems, and hardware teams to guide integration of learned policies
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other duties related to the research program. Job Requirements: A PhD in Computer Science or a relevant field. Strong background in deep learning, Generative AI, and multimodal learning. Strong
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Intelligence, Machine Learning, or relevant fields. Strong theoretical research capability, particularly in the theoretical analysis of optimization, convergence, stability, and/or generalization of deep
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cellular findings with human genetic and phenotypic data. A particular strength of this position will be the opportunity to work across both deep mechanistic projects and focused functional studies arising
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and remote sensing imagery for ecosystem monitoring. Develop machine/deep learning-based workflows to interpret ecosystem disturbance. Synthesize model simulations and multi-source observations
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differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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immunity, or animal models of chronic inflammation Expertise in advanced immune phenotyping (e.g. spatial omics, single‑cell omics including deep learning, O-link proteomics, Crispr screening, human PBMC