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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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), providing extensive industry exposure, access to real-world operational data, and insights into large-scale O&M practices. • ETH Zürich (Switzerland), supporting academic collaboration in SHM data processing
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area of finance including scholarships within special focus areas. These special focus areas include big data in finance, pension finance, and household finance. The application process is a joint
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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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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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Declaration of interest regarding PhD project within the field of biomarker and therapeutic targe...
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 interventions, longitudinal
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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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formation, growth and morphology of the layer play a big role, which is also a function of surface property of the substrate such as surface energy, roughness etc. This PhD project aims to systematically
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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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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