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Field
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Job Description Are you passionate about renewable energy and eager to apply machine learning to real-world challenges? Join our research team at DTU and work on groundbreaking advancements in
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many research synergies coming together on the main thread of machine learning and Artificial Intelligence (AI). The successful candidate will join the newly established research group AI in
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spoken and written English, and robust conceptual and analytical skills, as documented through course work or research projects. Attestable background knowledge, e.g., through course or project work, in at
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modeling, optimization, and/or data analytics. Description of the PhD topic (subproject C1 - New metrics for transportation system appraisal): Transportation systems must deliver “function” (i.e
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that activity-silent mechanisms, such as short-term synaptic plasticity, also play an important role. We will experimentally target these two mechanisms, using EEG in combination with machine learning to reveal
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Maguire. Training and Mentoring: You will learn a multidisciplinary set of practical skills including reaction kinetics, enzymatic chemistry, analytical techniques (e.g. NMR / fluorescence / UVVis
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, innovative, and dynamic research projects. They will learn the routine lab techniques and build strong analytical skills. The work between the two labs is at the interface of biology and engineering, employing
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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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chemical reaction networks with robotic systems and analytical science. You will also learn how to programme robotic systems and how to implement aspects of deep learning and neural networks for reservoir
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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