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the technical and social challenges of Advanced Air Mobility (AAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in
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Deutsche Forschungsgemeinschaft within the Reinhart-Koselleck programme for innovative high risk-high gain research. Requirements: university degree in chemistry or physics and profound knowledge in
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holidays and weekends (e.g. between Christmas and New Year) Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 3 days ago
Assistant position, with the successful candidate embarking on a PhD programme at LSHTM. It is anticipated that the role will lead to a further 18 month funded opportunity at Max Planck Institute
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social challenges of Advanced Air Mobility (AAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly
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(100%). Participants can write the PhD thesis in English or German. Programme duration 6 semesters Beginning Only for doctoral programmes: any time Tuition fees per semester in EUR None Combined Master's
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in the language agreed upon with his or her supervisor. Full-time / part-time full-time Programme duration 6 semesters Beginning Winter semester Application deadline The deadline for applications is 1
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the Reinhart-Koselleck programme for innovative high risk-high gain research. Requirements: university degree in chemistry or physics and profound knowledge in computational and theoretical physics/chemistry
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in the project, including experts in genetics, genomics, rhizosphere biology, and shoot biology to use a whole-plant-systems approach. Your tasks: Root anatomical phenotyping using imaging and
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, mechanical engineering, physics or similar basic programming skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g