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for personal and professional development Close collaboration between Empa and EPFL, bringing together complementary expertise in energy systems, machine learning, and foundation models The opportunity to shape
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NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration with
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an established code (for example VASP, Quantum ESPRESSO). ● Research experience performing molecular dynamics simulations. ● Experience training, fine-tuning, or validating machine-learning interatomic
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
serve more than a thousand undergraduate students and hundreds of graduate students and postdoctoral fellows. Our driving research ideal is collaboration-as it has been since the founding of our
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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learning (RL). 2) The Automation Group: Experts in designing and deploying custom robotic systems engineered for high-throughput sample synthesis, processing, and analytical characterization. This role is
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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well as collaborate with our content and education teams where they will undertake trials with partner schools, evaluate learning outcomes and user experiences, and contribute to the deployment of successful approaches
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development, and scientific exchange A collaborative and inclusive culture where you can grow your research career and belong Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov
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design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale microbiome and genome