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Field
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independently, outstanding team skills, and organizational aptitude round out the ideal profile. We offer you a thorough induction into your area of responsibility a diverse range of training and development
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development. Course organisation Certificates for specific modules are awarded Yes International elements International guest lecturers Language training provided Projects with partners in Germany and abroad
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, knowledge-based societies. Specialised research agendas have been grouped into four thematic pillars at BAGSS: Education, Personal Development, and Learning from Early Childhood to Adulthood Educational and
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of ISAS and the Leibniz association Training and scientific development opportunities in an international environment and an excellent working atmosphere in a very dynamic and professional team Extensive
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 1 hour ago
environment is provided to prepare students for the doctoral process and post-doctoral professional development through a compulsory curriculum of 30 ECTS. Doctoral students receive high quality supervision
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Effective communication skills and an interest in contributing to a highly international and interdisciplinary team Motivation for academic development, supported by bachelor’s and master’s transcripts and
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Effective communication skills and an interest in contributing to a highly international and interdisciplinary team Motivation for academic development, supported by bachelor’s and master’s transcripts and
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Research Center Borstel - Leibniz Lung Center | Hamburg Gro Borstel, Hamburg | Germany | 2 months ago
nextgeneration sequencing platforms, bioinformatics resources, and biosafety level 2 laboratory facilities, offering excellent conditions for doctoral training and scientific development. YOUR QUALIFICATION
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, which might include analysis and comparisons with the dynamics in tropical countries (VBD-mode, funded by BMFTR; https://clinicalepi.de/projects/vbd-mode.html) Collaborating closely with national and
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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