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Field
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focused on the development and application of recycled and waste-derived materials for advanced engineering systems. The role will emphasise computational modelling, simulation, and data-driven approaches
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interpretation of data Simple process simulation and modelling General lab organization and coordination Assist supervising PhD, Master and undergraduate project students Data collation, presentation and reporting
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and ethical guidelines. About you Successful applicants will have: a PhD (or near completion) in computer science, statistics, engineering, or a physical, environmental or life science discipline
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Investigate materials (electrode substrate, membrane and catalysts) Synthesis and interpretation of data Simple process simulation and modelling General lab organization and coordination Assist supervising PhD
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Research Fellow (Software, AI & Autonomous Systems) Required Qualifications PhD in Robotics, Computer Science, Artificial Intelligence, Electrical, Computer Engineering, or related disciplines. Key
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, data analysis and evaluation, library research, and reporting of findings. About you The successful candidate should hold or be about to receive a PhD in Physics, Chemistry, Functional Materials
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measurements to systematically investigate the relationship between composition, entropy level, crystal structure, and energy storage performance. Holding a PhD (or close to completion) in Materials Science
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Have you completed your PhD? Are you eager to enrich knowledge
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of Social Sciences’ PhD program . The Master’s degree must be of high quality (grade A or B) Fluency in Norwegian or another Scandinavian knowledge as this is necessary for working with the empirical material
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networks. Data Collection: Conduct in vivo neurophysiological recordings and behavioral assays in mice and tree shrews. Data Analysis: Process large-scale neural datasets, applying computational tools