79 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" scholarships at Technical University of Munich
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affect plant performance across environments. The project follows an integrated experi-mental approach across laboratory, greenhouse, and field scales, combined with advanced phenotyping and data-driven
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details of two referees The deadline for application is March 1st, 2026. For more information about PFT group, please check on our website Particle and Fiber Technology The position is suitable for disabled
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publications Letter of motivation, maximum 1 page Degree certificates and academic transcripts Contact details of two referees The deadline for application is March 1st , 2026. For more information about PFT
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imaging. Your Profile: The successful applicant must have the following: • Master’s degree in physics, biophysics, biomedical engineering, computer engineering or electrical engineering. • Excellent track
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, Mechanical Engineering, Electrical Engineering, Computer Engineering, or a closely related discipline. Strong research interest in telerobotics, shared control, human–robot interaction, or networked control
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preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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groups. Applicants with disabilities will be given preference in case of equal qualifications. More information about our research and team can be found here: www.lse.ls.tum.de/fmp. Application – Please
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at the interface of quantum information, condensed matter and high-energy physics. A strong background and interest in one or more of the following topics is desirable: - Quantum computing with qudits, quantum error
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-inflammatory features Key Responsibilities: • Conduct wet lab experiments to generate data describing the context-dependent Treg identity and underlying transcriptional networks. • Collaborate with project