156 algorithm-development-"Multiple" "NTNU Norwegian University of Science and Technology" PhD positions in Denmark
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and
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for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic
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expertise in autonomous marine systems. The research focus will be on development, implementation and verification of novel algorithms for motion planning and control of autonomous underwater vehicles. You
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development and marine management. Your primary tasks will be to: Compile and harmonize data from multiple sources (e.g., EMODnet, Copernicus, fisheries surveys, citizen science). Engage with data managers and
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better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past
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electricity price signals, demand-response mechanisms, and time-of-use optimization. AI-Driven Optimization using Reinforcement Learning: Apply RL algorithms to develop and train agents that optimize power
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of Science and Technology (NTNU) offers a joint 3-year PhD fellowship. Novel non-target chemical analyses have recently revealed that groundwater and drinking water are contaminated from PFAS, pesticide and
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and