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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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Posting Details Position Information Fiscal Year 2026-2027 Position Title Postdoctoral Associate, Materials Design and Innovation Classification Title Postdoctoral Associate Department Department
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Posting Description POSTDOCTORAL ASSOCIATE, Mechanical Engineering - Prof. Rohit Karnik and Prof. Zachary Smith, to work on experimental measurements, process design, and techno-economic analysis
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learning systems, including commercial products and research prototypes. The successful candidate will work alongside our dedicated software engineering team to design and implement a prototype AI tutor as
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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Technologies. The researchers will develop intelligent agents that collaborate, verify recommendations and learn from experience to improve wireless network energy efficiency. The research combines large
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landfill. We are looking for a passionate researcher who can undertake mechanical design, design and conduct experiments, learn quickly, solve complex problems, develop innovative solutions, and contribute
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control) Experience applying machine learning/AI to engineering systems Proficiency
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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