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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital
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statistical analyses for the tasks. Based on your competence and interests, your tasks will include: Develop and use epidemiological models (for example regression models or SIR-models), including for “what
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Postdoc in Psychiatric Epidemiology: Linking Register and Trial Data to Study Postpartum Depressi...
closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy for mothers with
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will collaborate closely with a PhD student and the broader project team and will contribute to the implementation and follow-up of the randomized controlled trial evaluating internet-based therapy
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: Essential experience and skills: You have a PhD in Bioinformatics, Computational Biology, Biostatistics or in a related quantitative field (e.g., Statistics, Mathematics, Physics), and a passion for problem
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forecasting. You will get the opportunity to participate and influence the development of advanced forecast solutions combining weather forecasts and novel machine learning/statistical forecasting methods
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to collaborate with fellow researchers, fostering a collaborative and innovative research culture. The ideal candidate has the following skills: PhD in computational biology, bioinformatics, computer science
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on developing machine-learning-based or statistical emulators to approximate key outputs of complex Earth System Models, with the aim of enabling efficient uncertainty quantification, sensitivity analysis, and
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optimizing and scaling up the emulsification and oxidizing protocols of polyolefin pyrolysis wax, as well as the proper control of an optimal oxygen transfer rate within the bioreactor. Moreover, statistical
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science. Your competencies PhD in computer science, statistics, data science, or a closely related field. Expertise in probabilistic machine learning, optionally generative and Bayesian models. A strong