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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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Postdoc in assessing carbon sequestration potential of different wetlands as nature-based solutio...
and scenario models Experience and understanding in statistical analyses. Collaborative skills and ability to participate actively in multidisciplinary teams A fondness for taking the initiative and the
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, or landscape modelling Further, we will prefer candidates with some of the following qualifications: Teaching and supervision experience at the BSc and MSc level Interest and preferably experience in developing
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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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, 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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at the Dynamical Systems Section is very wide ranging. From foundational research in work on statistical forecasting, modeling of spatial and temporal processes and time series analysis to applied research in wind