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of Technology (TU Delft) is hiring a doctoral candidate (4 years) on the subject of “statistical surrogate models of flexible energy systems”. Numerical models are at the core of the successful operation of
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) on the other hand. We use tools from statistical physics, information theory and non-linear dynamics to understand the how well a particular system responds to a stimulus, and how this stimulus is processed
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challenges in such large-scale distributed networks i.e., the low-cost sensing, decentralized statistical inference, distributed control and online decision making. These distributed systems will have to fuse
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, Statistics/Mathematics, Data Science, International Development, or a related field (essential). Experience working with large datasets, models and (geospatial) programming skills (e.g. preferably Python or R
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of this position is the theoretical foundation of statistical machine learning, with applications to satellite-based imageries. Candidates are required to have a strong background in mathematical statistics, signal
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regeneration methods. Explore laboratory data using statistics and adsorption models to understand underlying trends, explore adsorption mechanisms and predict performance. Investigate sorbent fouling and
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to the research topic; A strong mathematical background in optimisation, statistical learning, linear algebra and probability; A solid understanding of machine and deep learning; An experience in programming in
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. statistical and econometric methods and machine learning algorithms and understand their performance and optimally integrate both techniques with benchmark datasets and applications in road safety for decision
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, atmospheric stability, and urban setting)? How can we best represent this variability in outcome with a statistical model and take its inverse? Is such framework adaptive and generalizable to changes in urban
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, problem-solving, programming, and analytical skills. Strong interest and experience in conducting empirical research (e.g., user studies), knowledge in inferential statistics and qualitative methods (e.g