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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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approaches. Machine Learning in Geotechnical Engineering: Utilising data-driven approaches to model and predict soil-structure interactions or other complex geotechnical problems. Reliability-Based
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Posted on Tue, 07/08/2025 - 23:12 Important Info Deprecated / Faculty Sponsor (Last, First Name): Wang, Li Stanford Departments and Centers: Biology Stem Cell Bio Regenerative Med Postdoc
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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infrastructure, with the state-of-the-art reinforcement learning and generative AI, to detect, prevent, and preemptively mitigate intelligent attacker vectors. Supportive Mentoring: The postdoc will be guided by
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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, particularly radionuclides, on a continental scale. The aim is to develop a new class of inverse Bayesian models, STE-EU-SCALE, combining innovative forward dispersion models, machine learning techniques, and
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in the analysis of nonlinear time-dependent PDEs and Operator Theory/Spectral Theory. Additional expertise in rigorous computer assisted methods (e.g. interval arithmetic) is a plus. Required