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privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
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(m/f/d) in the topic: “AI-based processing of CAD models for automated planning of computer-aided manufacturing.” The candidate has the opportunity to pursue a doctoral degree (Ph.D.). Remuneration is
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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, medical informatics, databases, data mining, machine learning, applied mathematics, biomedical modelling and analysis of complex networks. Joint data science projects between the different partners
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 1 hour ago
information on BTU scholarship opportunities on our websites: https://www.b-tu.de/en/international/international-students/help-advice-on-all-aspects-of-studying/scholarships-1 . Academic admission requirements
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information please refer to: https://www.wmi.badw.de/fileadmin/WMI/JobAnnouncements/2025_11_06.PhD_position_KF_2026.pdf The position is suitable for disabled persons. Disabled applicants will be given
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research in the broad field of computational methods for the built environment. Particular emphasis is placed on building information modelling, digital drawings, point cloud capturing and processing as
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Albrecht (Administration) recruiting.leibniz-lsb@tum.de. We look forward to receiving your application! Data Protection Notice: As part of your application for a position at Leibniz-LSB@TUM, you will
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for the built environment. Particular emphasis is placed on artificial intelligence in construction, building information modelling, point cloud capturing and processing, as well as construction robotics. The PhD
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its maintenance and safety increasingly depend on data. This PhD project will develop new methods that combine remote sensing, physics-based modelling, and Bayesian machine learning to support risk