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Information Technology Are you interested in working with probabilistic machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models
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, benchmarking and deploy them for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning
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generation of atomic structures and training of machine learning interatomic potentials (MLIPs) in a way that maximizes the capability of high-performance computing (HPC) platforms available in the Oak
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involve building and applying state-of-the-art machine learning approaches that includes foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single
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involve building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the lab’s core research areas. The position
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, machine learning and deep learning methods, and the responsible conduct of research. Training: The candidate will receive training in implementing advanced machine learning and deep learning models, and NLP
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Information Technology Are you interested in working with machine learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron
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, Artificial Intelligence, Machine Learning, or a related field Exceptional BSc candidates with strong engineering experience will also be considered Experience in AI and neural network architectures Strong