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the research group and will work with collaborators at other institutions (nationally and internationally) The successful candidate should have the following qualifications: PhD in Epidemiology, Data Science
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well as machine learning and multivariate decoding of neuroimaging data to predict subjective experiences and individual differences. Successful candidates will be supported in building a research program at the
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training PhD in a relevant field of study and completed within a well-established research environment Languages Proficient in written and spoken English Technical skills Fully fluent in techniques
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, building engineering, or environmental engineering. Qualification requirements The applicant is required to have a PhD degree in energy engineering, building engineering, mechanical engineering, or other
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will collaborate with dedicated colleagues, have access to modern research infrastructure, and enjoy strong opportunities to attract external funding. You will have the space to make a real difference
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will collaborate with dedicated colleagues, have access to modern research infrastructure, and enjoy strong opportunities to attract external funding. You will have the space to make a real difference
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will collaborate with dedicated colleagues, have access to modern research infrastructure, and enjoy strong opportunities to attract external funding. You will have the space to make a real difference
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to study important processes like reversible photopolymerization and click-polymerization. Finally, we will use the information-rich data provided by I-TR-NUS&TR-L to make the automatic optimization setup
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manufacturing techniques is the key to improved heat transfer mechanisms and more efficient use of energy. Approximately 25 colleagues work at the division, including 15 PhD students. On the international level
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for PDRA to join the DQBF project: https://github.com/Sat-DQBF . The candidate will work on the development of DQBF solvers. The last few decades have seen tremendous progress in the development of SAT