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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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characterization (PXRD and Rietveld refinement, SEM, XPS, etc) Experience with machine learning techniques, particularly machine-learned interatomic potentials, in the context of chemical research. Programming
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Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote sensing and open-source GIS tools (e.g. Google
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, GIS tools and quantitative/spatial approaches including machine learning to understand how wildlife species are reacting to climate change at continental or global scales. Specific project ideas could
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further develop research software, such as simulation environments, data pipelines and infrastructure, for the training and evaluation of machine learning models within distributed AI. Contribute to and
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) Experience with safety PLCs and functional safety practice (e.g., IEC 61508 or ISO 13849) Experience with digital twins or simulation-based testing of automation sequences Experience with computer vision for
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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spectroscopy. Experience designing custom experimental hardware, vacuum systems, and plasma diagnostics. Experience applying machine learning methods for predictive analysis. Expereince with the Python
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 14 hours ago
Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible