72 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Technical University of Munich
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of machine learning approaches. Defining standards and databases for experimental protocols and biosystem designs will be of critical importance for the establishment of the Munich Repository of Standardized
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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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to joint research activities, publications, and surveys. Requirements PhD degree (or near completion) in robotics, control, machine learning, or a related field; Strong publication record demonstrating
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(MDSI) is an integrative research institute at the Technical University of Munich (TUM), with an interdisciplinary and cross-faculty focus on data science, machine learning, and artificial intelligence
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training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and familiarity with machine learning
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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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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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep learning to push our
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systems in a targeted manner. An important aspect of this effort is the accurate organization, harmonization, and exchange of research data that will fuel the application of machine learning approaches