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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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given significant consideration in the selection process.Essential qualifications- Completed (or near-completed) Ph.D. in economics, statistics or a related quantitative discipline by the start of
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, VUB and INBO. Profile We are looking for a highly-motivated candidate with a MSc degree in Biology, Ecology, Bio-Engineering, Environmental Engineering, Geology, Physical Geography, Earth Sciences
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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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models into Chalmers’ bridge simulators in collaboration with other researchers. You are also expected to supervise PhD and MSc students and to publish at least two peer-reviewed journal articles during
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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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, collaborate closely with geneticists, breeders and industry partners and publish your results in high-impact journals. You supervise MSc/PhD students and contribute to new grant proposals. Your qualifications
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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