15 software-engineering-model-driven-engineering-phd-position Postdoctoral positions in United States
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: PhD in nuclear engineering, applied physics, computational science, or a closely related field; demonstrated experience in Multiphysics modelling, numerical methods for PDEs, and code verification and
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fabrics, and a strong interest in AI driven models for the creation of a digital twin for textile. It is best suited for those able to work independently with limited oversight while remaining highly
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their career goals. DBMI has a dedicated team for Grants Development and Scientific Writing, and a Software Engineering Team that can provide invaluable feedback to improve the quality of your code and your
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, the appointee will help ALCF enhance the performance of AI-driven applications and HPC workloads, ensuring efficient utilization of resources and improved system predictability. Position Requirements Required
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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post
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. Contribute to open-source software development initiatives for Department of Energy projects. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years in
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research attention has been dedicated to understanding threats that climate change-driven increases in wet-bulb temperature pose to humans. More such science is also needed when considering the potential
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position to work on development and scaling of the data infrastructure and software for AI applications on supercomputing systems and AI testbed systems. The postdoc will work on multimodal data management
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance