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to impactful, data-driven research with a global perspective, and help improve healthcare systems and address inequalities. Under the supervision of Professor Maarten J. IJzerman and co-promoter Dr. Nora Franzen
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Leibniz Institute of Ecological Urban and Regional Development (IOER) • | Dresden, Sachsen | Germany | about 5 hours ago
Dresden University of Technology Course location Dresden In cooperation with Dresden University of Technology Teaching language English Languages The programme is conducted in English. Full-time / part-time
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: The successful applicant will receive a tax-free stipend, at the current value of $36,063 per annum 2025 full-time rate, as per the Monash Research Training Program (RTP) Stipend www.monash.edu/study/fees
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of forests in climate change are now key social issues that require more knowledge. In order to both sustainably use and safeguard forest biodiversity, a coherent basic science research program is needed
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chemistry) laboratory; statistical skills for proper data treatment ; experience with programming (R or Python) for data handling and visualisation; strong analytical skills; excellent communication skills
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failures. We offer access to unique experimental data and computational tools developed by our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel
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background, we will invite at least one applicant from each of these categories to an interview. Contact information For questions about the position: Professor Nina Reiners, Norwegian Centre for Human Rights
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information Further information may be obtained from Johanna Vorwerk, Assistant Professor at DTU Wind and Energy Systems, vorjo@dtu.dk . You can read more about DTU Wind at www.wind.dtu.dk . If you are applying
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in real-world applications for energy storage and conversion. Tijs Karman Assistant professor in Theoretical and Computational Chemistry, Radboud University, Institute for Molecules and Materials Tijs
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, Industrial Engineering, Computer Science, or Machine Learning. Solid experience with quantitative optimization methods, including (but not limited to) mathematical programming and stochastic dynamic