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Field
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learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model parameters that have been learned from data. For instance
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therefore be part of a particularly dynamic research effort. As part of the JET2SB project, funds have been allocated for: - the acquisition of the computer equipment necessary for the research work
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respect to AM printing parameters. The project will also be able to generate suggestions for the design of new alloys to be produced via additive manufacturing as well as advise on protective coatings. By
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of manipulation tasks) as well as observability (to guarantee an accurate global state estimation) and robustness against modeling errors. Another idea is hinging on the concept of closed-loop sensitivity
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lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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various objectives (model tuning), such as energy indicators (e.g., efficiency, exergy analysis) or parameters affecting investment costs (e.g., heat exchanger pinch, fluid flow rates), in order to build a
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equivalent) in Media Studies, Digital Humanities, Visual Culture, Human-Computer Interaction (HCI), Cognitive Psychology (with a focus on perception or sensory studies), Immersive Media / Virtual Reality
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the proportion of individuals within different ethnic groups classified as high risk. - Develop multistate survival models (MSM) to estimate transition parameters between cancer progression states across risk
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter