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: Appropriate PhD in related field Knowledge, Skills, and Abilities: Familiarity with appropriate laboratory and technical equipment; ability to effectively use a computer and applicable software to create data
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testing data Development of machine learning models for battery health assessment and remaining useful life prediction Job Requirements: PhD degree in Electrical Engineering or related subjects. Expert
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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expertise in climate science, hydrology, earth and planetary science, and physically-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes
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-based knowledge with machine learning. You will work closely with the Utrecht University team and OpenGeoHub together with other project partners, to develop and implement surrogate and hybrid modelling
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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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research into planet formation/protoplanetary discs or the ISM/star formation and may also have some experience in statistical methods and/or machine learning. Dr Winter and QMUL are committed to improving
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning