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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 4 hours ago
of the research areas offered , also allows doctoral students to contribute their own institutional experience. A structured and international learning environment is provided to prepare students for the doctoral
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Associate / PhD Student (m/f/x) (subject to personal qualifications employees are remunerated according to salary group E 13 TV-L) starting October 1, 2025. The position is limited until June 30, 2029
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the research training with outstanding opportunities for career development, continued education, and life-long learning. We offer excellent working conditions in a young and interdisciplinary team. In an open
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periods Please find more details on this website . Submit application via Please apply via e-mail . Possibility of finding part-time employment The university supports students in finding part-time jobs in
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environments including a stay at another research team. Obtain experience with teaching or other types of dissemination related to your PhD project. Teach and disseminate your knowledge. Prepare and submit a PhD
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culture ticket; see details here . Costs of living Studying in Jena is tuition-free. Costs (per month) vary depending on individual circumstances: rent and food: 550 EUR, books and learning materials: 50
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: rent and food: 550 EUR, books and learning materials: 50 EUR, health insurance: 120 EUR, other expenses: 180 EUR, total: 900 EUR. Funding opportunities within the university Yes Description of the above
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scientific results, talented PhD students need to acquire knowledge and are also required to exchange knowledge and experience with other PhD students in method-oriented working groups. Supervision by two
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning