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energy modeling and analysis to be part of the Ports as Energy Transition Hubs (POTENT) Marie Sklodowska-Curie Actions Doctoral Network. The network will consist of 15 PhD candidates interested in
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diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge energy modeling and analysis to be part of the Ports as
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, you will: Design and perform fluorogenic and nano-photonic DyeCycling experiments. Write/adapt analysis code to process fluorescence trajectories and extract kinetic information. Evaluate bioconjugation
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), Deep Neural Networks. Probabilistic Machine Learning and Time-series Analysis. Industrial applications of AI (energy, process industry, automation). Software development experience in teams. Programming
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ideally includes past or present projects centered on machine learning analysis of chemical or biochemical data, especially relating to structural biology. Ample experience with Python programming is
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and utility systems necessary for their operation (e.g., electrical, water, telecom., transport networks); perform an integrated multi-hazard fragility and vulnerability analysis of CI using different
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scientists. * Excellent oral and written communication skills. Preferred Qualifications * Familiarity with common NGS analysis tools. * Familiarity with tensorflow and/or pytorch.  * Demonstrated ability
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Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute • | Jena, Th ringen | Germany | about 5 hours ago
of professors and group leaders of different disciplines from all participating institutions forming a highly interactive network of scientific discourse and experimental work. The ILRS is part of the excellence
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to communicate in simple language as well as a willingness to engage with bioethical issues and participate in the public discourse they may provoke. International students are offered the opportunity
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from a technical and scientific perspective. The Ph.D. candidate will develop methods for uncovering DNS infrastructure dependencies at scale, relying on active measurements and graph-based analysis