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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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, electrolysis, power-to-x, batteries, and carbon capture. The research is based on strong competences in electrochemistry, atomic scale and multi-physics modelling, autonomous materials discovery, materials
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No EEO Category I-Faculty Full Time or Part Time Part Time Does this position have telework options? -Telework options are subject to change based on business needs- No Does this position have a bilingual
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Personnel No EEO Category I-Faculty Full Time or Part Time Part Time Does this position have telework options? -Telework options are subject to change based on business needs- No Does this position have a
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means contributing to a better tomorrow. Whether you are a current resident of our New Haven-based community- eligible for opportunities through the New Haven Hiring Initiative or a newcomer, interested
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, Criminal, Education Working Conditions: The position is based on site in Cambridge, MA. There may be an opportunity for a hybrid work model compliant with Harvard University policies. It is anticipated
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/or dynamic analysis of mechanical/robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the-loop
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Postdoctoral position in the development of an AI-based phenotyping system for high-throughput sc...
Position overview We are seeking applicants for an 18-months postdoctoral fellowship focusing on the development of an AI-based phenotyping system for high-throughput screening for aphid
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environmental quality. The fellow will contribute to field and laboratory studies, data analysis and modeling, publication of results, and development of decision-support tools for producers, advisors, and agency
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a high level of evidence, yet their narrow eligibility criteria and controlled settings often limit generalizability. Advances in causal inference and the increasing availability of real-world data