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, distributed machine-learning frameworks, and HPC workload managers. Experience using GPU-accelerated computing platforms and software stacks such as NVIDIA CUDA or AMD ROCm. Experience developing and
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, engineering and the life sciences. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit
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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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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Machine Learning Engineer | Neurobiology | Dr Albert
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Science, Computing, Data Science, Artificial Intelligence, Engineering, or a related discipline. Relevant professional certification(s) in Artificial Intelligence (AI), Generative AI, Machine Learning, Data
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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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interdisciplinary research that integrates modern empirical methods, large-scale administrative and textual data, and advances in artificial intelligence and machine learning. Research conducted at NBS regularly