218 computational-physics "https:" "https:" "https:" "https:" "Caltech" positions at Technical University of Munich
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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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stimulating work environment that promotes individual creativity and effective teamwork with a clear focus on quality output. More in-formation about the group is available at http://pur.wzw.tum.de
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verschiedene Projekte in Bereich E-Mental Health durch (weitere Informationen unter https://www.sg.tum.de/digitalhealth/startseite/ ) The position is suitable for disabled persons. Disabled applicants will be
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academic supervision from Prof. Henkel. You will participate in the doctoral program of the TUM School of Management; after about a year, there is the possibility to apply for the School’s Academic Train-ing
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding
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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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of novel catalysts by electrochemical flow cell measurements that will be coupled to on-line analytics (c.f. https://www.nature.com/articles/s41563-019-0555-5). Specifically, an in-house designed flow cell
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. Requirements: Completed university degree in computer science or applied mathematics, remote sensing, geophysics, physics, or related areas Expertise in computer vision and/or machine learning (deep learning
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
for sustainability, new concepts for security and solutions for current latencies in communication networks. Find out more about the project under https://6g-life.de/ About us: At the Chair of Information-Oriented
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for excellently-qualified graduates with a master's degree in computer science, electrical engineering, data sciences, geoinformatics, statistics, mathematics, physics or comparable. Further, the successful