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collaborations are regarded as an integral part of the PhD research experience and are explicitly encouraged (e.g. South America, Asia or Africa). The PhD process will be accompanied by integration into TUM’s
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to them. For this purpose, geometry-driven AI-based methods are to be researched and developed in order to be able to automatically generate the corresponding process steps and parameters using CAD design
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papers Contributing to educational events, such as courses and hackathons Your Profile: Excellent Master or preferable subsequent PhD degree in Computer Science, Mathematics, Physics, or similar fields
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Junior Research Group in the field of Metabolic Plasticity. What we are looking for in applicants: We are looking for established scientists in an early career phase (up to 6 years post-PhD) who
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available in the further tabs (e.g. “Application requirements”). Objective A cooperation agreement on the joint training and education of PhD students has existed between the Chinese Academy of Sciences (CAS
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responsibilities will include utilizing state-of-the-art ML platforms such as TensorFlow and PyTorch to process and analyze complex datasets related to CO2 transport and storage. You will develop methods for real
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international conferences Mentoring of researchers Your qualifications: University degree in sociology, economics, or a closely related field Doctorate / PhD in one of the above-mentioned fields is an advantage
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offers the opportunity for acquiring a PhD degree under supervision of Alexander Libman and Tomila Lankina at the FU Berlin. Requirements: -completed academic university education in a relevant field
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: Implementation of a PhD project in the area of peace operations and conflict management with a focus on the role of the United Nations Presenting research findings at international academic and policy meetings
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basic knowledge of French and fluency in English. Have a clean driver's license. Successful volunteers are trained by and work closely with field managers, researchers, PhD students, and local field