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Are you interested in advanced rheological characterization, microstructure analysis, and modelling of complex food systems? In this postdoctoral project, you will combine fundamental science with
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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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, addressing practical challenges in flow distribution, heat and mass transfer, sealing, reproducibility and stable operation. Translate device concepts into CAD models and engineering drawings, and coordinate
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make a difference in the world! Position Information Texas A&M AgriLife Research at Temple is seeking a highly motivated scientist with expertise in crop modeling, remote sensing, and geospatial
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artificial intelligence. Potential research topics include the mathematical and statistical analysis of modern generative models, particularly diffusion- and flow-based models, as well as their applications
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modeling approaches. The successful candidate will design and conduct experiments, develop and implement analytical and numerical modeling approaches, and perform microstructural characterization to evaluate
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learning (ML) to address future physics and detector challenges. Current physics interests include Standard Model measurements and searches for new phenomena. We welcome applicants who are excited
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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) project, an interdisciplinary and multi-institutional collaboration focused on disturbance-driven ecosystem transitions and their impacts across the U.S. Gulf Coast. EGRET employs an integrated model
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing