43 programming-"Multiple"-"Prof"-"Humboldt-Stiftung-Foundation"-"U.S"-"FEMTO-ST-institute" "O.P" positions at Technical University of Munich
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview
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interested individual with the potential to become a leader in the team. Intellectual flexibility and the ability to work in a multidisciplinary environment on multiple scientific and infrastructure projects
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integration as well as programming are essential. Additional important requirements include a sound understanding of biology. We are looking for a self-motivated, highly skilled and broadly interested
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research funding • Strong organizational and management skills, with the ability to manage multiple tasks in parallel • Excellent analytical skills and ability to present interdisciplinary research
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cooperations efficient and productive. As Research Associate you will also support our teaching activities in several Bachelor and Master programs offered by the School of Engineering and Design and the School
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or Postdoc Position in Numerical Mathematics m/f/d, 100%, 2 years+ As part of the second phase of the DFG funded Priority Programme SPP2311, the Chair for Numerical Mathematics under the leadership
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hubs for STS, we are a lively intellectual community of 80+ researchers from numerous disciplines and fields of specialization. As a department, we deliver 2 Master’s programs and design STS content
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participation in the related Graduate School training programs. Qualifications The applicants should possess: an excellent or very-good university degree in economics, business studies, agricultural sciences with
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to contribute to groundbreaking work in our research area. About the Program: We are implementing a program that creates an additional two year postdoctoral position exclusively for female researchers working in
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning