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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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positive and supportive team environment. You prefer to stay organized and take pride in completing your tasks in a thoughtful and structured way. As a formal qualification, you must hold a PhD degree (or
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PhD students. Contributing to the teaching at the department to build your teaching portfolio for applying to academic positions. Participating actively in the research community, including attending
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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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: PhD in computer science, machine learning, operations research, transportation engineering or a related field. Programming skills in C/C++ and Python, along with experience working with simulation
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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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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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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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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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. Additionally, your resume must comprise: A PhD or equivalent Research experience in Software Engineering or Digital Twin & Digital Transformation Track record of publications. Ability to work collaboratively in