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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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variables, fixed effects for panel data, matching estimators, or machine learning) or other advanced statistical modelling.- Advanced programming skills in Stata, R, Python or a similar software.- Strong
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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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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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
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Essential Criteria: Qualifications A good first degree in Computer Science, Machine Learning, Maths and Statistics, Robotics or a related subject. A PhD (or an MSc with extensive research experience) in