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Field
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for the conceptual framework, design, and implementation of these machine learning models, ensuring trustworthy computations and scalability on the DOE’s leadership computing facilities. The focus will be
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related to modelling of the optical response of materials on the atomic scale. The successful candidate (m/f/d) will join a progressive Light and matter theory group at the Institute of Physics (FZU
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and training your own AI-based models for image segmentation or image compression, as demonstrated by Git repositories Experience in supervising students and young scientists Good knowledge of materials
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conceptual analysis and are motivated to contribute to applied sustainability challenges. On top of that you have: A PhD degree in Chemical Engineering, Polymer Chemistry, or a related field. Strong background
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to contribute to the development of fundamental aspects of computer science (models, languages, methods, algorithms) and to develop synergy between the conceptual, technological and societal challenges associated
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. Conceptualize and implement digital twin systems for packaging and supply chain decision-making. Integrate optimization, AI, and machine learning methods into models of logistics and lifecycle sustainability
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involved in other collaborative research projects between Coley’s Conceptual Organization, Reasoning, and Education Laboratory (CORE Lab) and Helmuth’s Marine Ecological Forecasting, Ecophysiology, and
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business model. In its research approach, the UM6P promotes transdisciplinary, entrepreneurship spirit and collaboration with external institutions for developing up to date science and at continent level in
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an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research approach, the UM6P promotes transdisciplinary
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-development and refinement of conceptual models; devising management scenarios; building network models in one or more platforms (e.g., loop analysis/qpress; fuzzy cognitive maps/Mental Modeler; Bayesian belief