173 postdoc-in-thermal-network-of-the-physical-building positions at Technical University of Munich in Germany
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flight physics code, which are to be further developed and supplemented as part of the project. In addition to code development, various phenomena of the landing approach to buildings shall be examined
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and be creative. With the help of extensive and goal-orientated professional development measures and career-building programmes we encourage you to grow as a person. To ensure a good work/life balance
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Program if you aspire to an academic career. We offer you access to an international research network by presenting your research at leading international conferences and spending a research semester at top
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an opportunity to make a significant impact by ensuring efficient communication and administration among project partners from different research disciplines and sectors throughout the CitySoundscapes project
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-cell structural analysis of actin assembly and force generation Your profile: - Excellent BSc in biology, physics, biochemistry, bioinformatics, or related field of science - Knowledge/experience with EM
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geoinformation from big Earth observation data acquired by current and future Earth observation missions. It is involved in a large number of third-party projects and a large international network. It is a global
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, day-to-day reliability, sustainability, and security against cyber and physical attacks. Key Responsibilities: Research and Analysis: Engage in extensive literature reviews to stay abreast of current
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models and neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio
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. In addition, you can further expand your network with partners from science and industry through our lighthouse projects. In your role as a post-doc, you will combine team- and institute-oriented tasks
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architectures which leverage our increasing understanding of the behaviour of neural networks trained with DP to ameliorate these trade-offs in biomedical applications. - Foundations of private machine learning