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. Close collaboration with NADARA will allow you to apply and validate your methods using real operational data from a large wind turbine portfolio. Project Responsibilities and Qualifications As a Doctoral
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DTU Tenure Track Researcher on Nanoreactors for Operando Visualizations of Nanoparticle Catalysis...
of statistical methods in quantification of EELS data, in particularly. Prior experience with advancing EELS measurements of captive gas (of nanoliter volumes within MEMS structures) is a big advantage, and so is
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is to develop RL methods that can search large policy spaces and support decision-makers in exploring robust strategies under deep uncertainty. Policy problems typically involve many control levers
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to the development and validation of novel blood-based biomarker assay. Mechanistic studies will be conducted in a neonatal large-animal model of cholestatic liver injury, where the student will participate in in vivo
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student to work within the ADaM project (Autonomous workflows for Data-driven first-principles Modelling). The project will leverage Large Language Models (LLMs) as active software agents to help automate
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population data will be considered a strong advantage. The ideal candidate is comfortable learning new analytical methods and working with large and complex datasets. Qualification requirements PhD stipends
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have an interest in quality news and AI-based chatbots. We expect: A Master’s degree in either computer engineering or computer science/data science. Experience with AI, Large Language Models, RAG, data
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is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here . You can read more about career paths at DTU here . Further information Further information may be
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part of DTU’s Tenure Track program. Read more about the program and the recruitment process here . You can read more about career paths at DTU here . Further information Further information may be
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). The project envisions the use of methods such as: Large Language Models (LLMs) to hold a knowledge base of regulatory documents Reinforcement Learning (RL) for adaptive decision-making under uncertainty Dynamic