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Max Planck Institute for Political and Social Science, Göttingen | Gottingen, Niedersachsen | Germany | 17 days ago
PhD degree is preferable in Sociology, but applications from closely related disciplines are welcome Excellent academic publication records appropriate to career stage Strong analytical and
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. This nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process seeks high-quality academics and professionals
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motivated team player who thrives in a collaborative and interdisciplinary research environment. The ideal candidate possesses a PhD degree in chemistry or chemical engineering, materials science, physics
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bioenergy, cultivation and conversion of micro and macroalgae, and geology and mining. Oriented towards Africa, ASARI acts in connection with a wide network of universities and research centers around the
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the impacts of rapid and longer term spatial agricultural shifts, due to international trade networks, on biodiversity, considering losses and gains and how this can be used to allow more direct comparison of
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collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include Expertise in several areas among
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Discovery Innovation Network foundation, NNF grant number: NNF23SA0088590. The position focuses on experimental development at the interface of microfluidic organ-on-chip systems and metabolic magnetic
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their networks and research profiles. There are funds to cover visa costs for successful non-UK applicants. If you are still awaiting your PhD to be awarded you will be appointed at Grade 6, spine point 30. Upon
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-ecological transition mechanisms, the diffusion of ideas and opinions, and the role of social networks and information channels. The work will be conducted in a comparative multi-city perspective, combining
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Qualifications: PhD (or equivalent) in Biostatistics, Epidemiology, or a closely-related quantitative field (by start date) Strong training in causal inference methods Experience working with large-scale