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environment? We have a vacancy for a PhD in (nano)materials science and environmental applications. Will you join our team? What will you do? The Mind_Gap programme is fundamental in nature and set to enhance
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interdisciplinary research. In addition, you possess: a completed MSc degree in economics, environmental science, or a related field; a solid foundation in applied quantitative methods (e.g., econometrics, spatial
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development. You will also apply a coastal dune model (AeoLiS) to dunes in the Netherlands and evaluate how well it replicates historical changes. You will share your results in stakeholder meetings, scientific
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PhD position on Closed-loop testing for faster and better EM evaluation of complex high-tech systems
transcript of B.Sc. and M.Sc. education. The selection procedure includes an interview and a scientific presentation. The first round of interviews will be held in week 35. For more information about the
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of ESA's climate activities. You will have the opportunity to work in cooperation with relevant scientific projects within ESA's CCI programme and other teams at ESA. In addition, you will be encouraged
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. Collaborate with top scientists and use your technical expertise to drive scientific progress. As a junior full-stack software developer, you will be part of the DevOps team and support senior developers in
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PhD position in the field of climate dynamics Faculty: Faculty of Science Department: Department of Physics Hours per week: 36 to 40 Application deadline: 31 August 2025 Apply now How does a
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facilitate the professional and personal growth of your team members Be responsible for the department’s vision and the scientific quality of its research Play a connecting role within your department, across
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vision and the scientific quality of its research Act as a connector within your department, the wider institute, and serve as a bridge to society and relevant stakeholders Manage the departmental budget
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across domains. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data