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Field
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: Develop AI-assisted experimental workflows for oxide semiconductor deposition, aiming to accelerate process optimization and unravel new process–property relationships, and contributing to an EU Horizon
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: - A PhD degree in optimization or systems and control theory. - A strong mathematical background and an interest in conducting theoretical research. - A proven track record. - Excellent English
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candidate will contribute to cutting-edge research in optimal control theory, differential game theory, mean-field game theory, and mean-field-type game theory, developing advanced mathematical frameworks and
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electrophoretic techniques. Purify viral and host RNA for advanced mass spectrometric analysis. Optimize and troubleshoot current experimental procedures. Train and lead a team of undergraduate and graduate
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circuits. The researcher will independently design and conduct experiments using preclinical models of SCI; develop, optimize, and troubleshoot experimental techniques and protocols related to spinal
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, optimization, and the experimental validation of communication-aware robotic systems. Position Requirements: Ph.D. in Robotics, Electrical Engineering, Telecommunications, Computer Engineering, Control
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University We are looking for a highly motivated postdoctoral researcher with a strong interest in optimization. The position will involve the development of novel reformulation and algorithmic methods
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Lehigh University Plasma Control Group (LU-PCG) and contribute to advanced control synthesis, neural observer development, scenario optimization, and AI-enabled digital twins for magnetically confined
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to iteratively refine models and optimize experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data
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, nonlinear optics, and the design, alignment, and optimization of optical setups. ● Experience with atomic force microscopy (AFM)-based infrared/vibrational spectroscopy or related nanoscale spectroscopy