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Field
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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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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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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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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
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related to heart failure and cardiovascular biology. Develop and apply machine-learning and deep-learning approaches to identify disease-associated cardiomyocyte subtypes, cellular trajectories, and
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include deep learning, reinforcement learning, differentiable modelling and inverse design. You will implement and evaluate these methods using experimental optical systems and work towards their
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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vitae - please clearly define your current status with respect to residency, fellowship, or PhD A brief statement describing your background, research interest, and reasons for applying to the fellowship