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: Sustainable mineral processing and hydrometallurgy, with emphasis on potash beneficiation, process optimization, and potash brine valorization. Main responsibilities: Conduct literature review on potash ore
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, Mohammed VI Polytechnic University (UM6P) is launching a one-year postdoctoral research position. The project focuses on testing and optimizing treatment processes that use calcium carbonate-rich materials
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materials and production methods for prosthetic devices. Design and execute experiments to optimize the manufacturing process. Collaborate with multidisciplinary teams to integrate technological advancements
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, process optimization, and potash brine valorization. Main responsibilities: Conduct literature review on potash ore processing (carnallite) and brine management practices; Perform mineralogical and chemical
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manufacturing of next-generation stack components. In this project, we aim to develop, optimize, and test an innovative monolithic component that aims to substitute conventional porous transport layers (PTLs) and
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on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities
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of complex mixtures, experimentally and/or theoretically. Strong background in conceptual process design, simulations tools (e.g. Aspen Plus and Aspen Adsorption), process optimization and TEA. Ability to work
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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the internal application process. Welcome to The Ohio State University's career site. We invite you to apply to positions of interest. In order to ensure your application is complete, you must complete
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computationally efficient at simulating permanent magnet structures, and capable of leading to the globally optimal solution of the underlying design optimization problem. We aim to combine gradient-based