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Academy, University of Gothenburg. General information about being a doctoral student at the University of Gothenburg can be found on the university's doctoral student pages. https://www.gu.se/en
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strongly desired Programming experience in Python and familiarity with machine learning frameworks Knowledge of emerging memory devices and computing technologies is beneficial Excellent communication
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Quantify and formalize uncertainty estimates to support reliable deployment in real-world building management systems For further information on the project you can contact your future superior: https
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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Qualifications Doctoral degree in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field. Excellent programming skills, as well as strong oral communication
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Qualifications Doctoral degree in statistics, biostatistics, computer science, applied mathematics, or a related quantitative field. Excellent programming skills, as well as strong oral communication
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whole fleet can act as one Virtual Power Plant. Read more about the project and other participants here: https://aegis-dn.eu/ Your role: You will be working on “End-to-End Energy Modeling for
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, biophysics or a related field with a specialization in soft matter science You have some experience in data analysis using standard software like Origin and in scientific programming Experimental experience
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with drones, GC systems, or trace gas observations (desirable) Possess solid programming skills (e.g., Python) for data analysis (helpful) Apply anyway even if you do not meet all criteria, as missing
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benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers ( HAW ) – UAW for purposes of collective bargaining and