112 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"SciLifeLab"-"IFM" positions at Technical University of Munich
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the main author. · Name and contact information of a referee. In case of technical questions, please contact Muhammad Hassan via email: muhammad.hassan(AT)cit.tum.de. As an equal opportunity and affirmative
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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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for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position
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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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biochemists developing the labeling agents, data analysts developing analysis algorithms and physicists developing hardware. The candidate The candidate should have a firm base in in vivo imaging and cell
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the VIOLET research framework Work closely with clinicians and data scientists to ensure the developed systems meet clinical needs and are validated against real-world scenarios Mentor and supervise PhD
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Robot Learning (ID: TUEILSY-POSTDOC20251219-RL) Robots that learn from data promise greater autonomy and performance, but their deployment in the real world hinges on the ability to guarantee safety