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Field
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neuroimaging (particularly functional) is required, and ideally the candidate will have experience in linking imaging methods with cognitive, neurobiological or computational modelling frameworks. Your key
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on post-training methods for these low-resourced languages, for example, by investigating the role of synthetic data, among other data augmentation techniques, and the role of in-context learning in
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production and quality control will help save natural resources as well as reduce waste material and energy consumption. Formulation and test methods using mathematical modelling and prediction tools. Fouling
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materials, especially under high-performance applications. Traditional methods for coating and stabilizing LFP cathodes often face limitations in durability and electrochemical efficiency. This research aims
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, depending on the discipline) Research Experience and Competence Alignment with the project’s topic Familiarity with relevant tools, methods, and techniques Soft and Organizational Skills Communication and
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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program and at the AU Summer University You will be under daily supervision by group leader Rasmus O. Bak, and you will formally report to the Head of Department Thomas G. Jensen. Your competences You have
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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Postdoc in development and testing of electrodes for liquid alkaline water electrolysis - DTU Energy
electrochemical testing by means of various voltametric methods and electrochemical impedance spectroscopy. As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical
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algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems