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Field
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researchers to develop and optimize novel techniques for the production of prosthetic devices. Responsibilities: Outline the key responsibilities of the position. This might include: Research innovative
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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optimization technologies are revolutionizing the way power grid is operated and planned. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using
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). The ideal candidate will have: A PhD in environmental science or closely related discipline by the start date of the appointment Broad understanding of eco-evolutionary optimality concepts and modelling
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singular elliptic and parabolic PDEs, free boundary problems, optimal control of free boundary systems with distributed parameters. Current areas of interest include Potential Theory, Harmonic Analysis
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researcher specializing in Sodium-ion (Na-ion) cathode materials to join our research team. This position focuses on the design, synthesis, characterization, and optimization of advanced cathode materials
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researcher specializing in Li-ion cathode materials to join our research team. This position focuses on the development, synthesis, characterization, and optimization of advanced cathode materials for next
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on the effective integration of renewable energy, resource efficiency, and waste reduction. The candidate must hold a PhD in Urban or Rural Development, Civil Engineering or related domain. The candidate is expected
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, modelling, optimization and validation, and focus on: Develop thermally integrated storage and conversion systems, including Carnot batteries and/or high-temperature heat pumps based on power cycles. Design
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor