11 msc-in-statistics Postdoctoral positions at Technical University of Denmark in Denmark
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including supervision of BSc and MSc students associated with the project As a formal qualification, you must hold a PhD degree (or equivalent). In the assessment of the candidates, consideration will be
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responsible for project milestones and deliverables. Teach and supervise MSc student projects For this position, experience with simulation development and coding is essential. We expect the candidate to have
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journals and conferences. Collaborating with academic and industrial partners, both nationally and internationally. Engaging in the mentorship and supervision of BSc and MSc student projects, co-supervising
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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equivalent), preferably within civil and environmental engineering, statistics, industrial ecology or data science with a passion for sustainability. We welcome candidates with postdoctoral experience
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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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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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, scipy, scikit-learn, pytorch, pytorch geometric, etc.). Proficiency in statistics and graph machine learning, including the ability to build and deploy models, and evaluate their performance. Software
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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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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