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). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications Experience with multi-GPU model training and large-scale inference. Familiarity with modern AI
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to work) in reduced order modeling, Causal inference and High Performance Computing are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation
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and neural activity. In particular, Postdoctoral AI Researchers may help develop brain foundation models that predict patterns of neural activity from large-scale, multi-regional recordings. Areas
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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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aim to advance the capability and applications of this technique to demystify activity at complex electrodes across multiple length scales (nano- to macro), enable new types of measurements, achieve
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-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims, linked with rich contextual information such as census
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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modern AI/ML methods to protein and cellular biology, including protein structure prediction, protein–protein and small-molecule–protein docking, cell state prediction from large-scale Perturb-seq datasets