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Field
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: PHD Thesis Job description: Your Job: Digital methods for inverse materials design are essential
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stack designs, operating strategies and measurement methods for power-to-X systems using high-temperature electrolysis (SOEC). The developed methods will serve to optimise the operation of SOCs
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Your Job: Design and development of an in-house X-ray beamline for long term operando investigation of chemical hydrogen storage reactors Unravelling of relationships between catalyst structure and
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staff position within a Research Infrastructure? No Offer Description Work group: Institute of Materials and Process Design Area of research: PHD Thesis Part-Time Suitability: The position is suitable
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Job related to staff position within a Research Infrastructure? No Offer Description PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1
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defined within different manufacturing techniques of lightweight structures to enable novel development of materials and process design. The PhD position will be supervised by Prof. Noomane Ben Khalifa
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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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and optimizing building designs will be developed. The goal of this project is to develop a methodology for automatically checking building designs against regulations, and then provide reasoning about
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discipline Apply: https://mgician.eu/research/doctoral-candidate-projects/dc3/ DC4: Atomistic Modelling and Design of Thermoelectric Materials and Interfaces Host: King’s College London (KCL), London
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. - Design and execute comprehensive testing phases for the microgrid, utilizing both synthetic load profiles and real-world operational scenarios. - Conduct in-depth Life Cycle Assessments (LCA) for a novel