1,748 evolution "https:" "https:" "https:" "https:" "University of Kent" positions in Germany
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action recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning
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recognition, and enable seamless collaboration between humans and machines. Long-Term Human-Technology Evolution: investigate the longitudinal impact of human-technology interaction on learning, behavior, and
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semester Application deadline Will be announced on webpage: https://www.mgse.econ.uni-muenchen.de/application/index.html Tuition fees per semester in EUR None Combined Master's degree / PhD programme No
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 20 hours ago
development and guidance. In this context, "customised" means in particular that the educational events are tailored according to individual needs of the participants. Of course, this implies in turn
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programmes Bridging functions between educational institutions, families, networks andneighbourhoods Participatory processes in formal educational contexts. Where to apply Website https://www.fh-potsdam.de/en
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of family education & counselling Educational qualifications with regard to digitalisation. Where to apply Website https://www.fh-potsdam.de/en/university-career/career/job-offers-fh-potsdam Requirements
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development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs SUPPORT
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part of the programme. Additional support on administrative matters and personal skills development is offered by the LMU GraduateCenter (https://www.graduatecenter.lmu.de ). Part-time studies
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data