71 digital-biosignal-processing PhD positions at Technical University of Munich in Germany
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Application process Send your application in English by email to amx@wzw.tum.de with title “Research Associate application” at latest 01.07.2025 including • A cover letter (for example elaborating your
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working on NLP for medical applications (see German version below / siehe unten für Deutsche Version) . Your Responsibilities Conduct applied research at the intersection of Natural Language Processing (NLP
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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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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the planning methods into the open data AgiMo Digital Twin, (4) develop participatory planning methods based on the technical outcomes from the digital twin to create future scenarios for responsible mobility
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paid PhD position in the area of Natural Language Processing starting as soon as possible. Your responsibilities Research & development projects in the area of NLU and NLG Contribution to teaching on
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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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improve industrial processes by establishing a thorough understanding of the materials at different length- and time scales. Your project Milk protein concentrates (MPC) are dairy proteins which
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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Our research focuses on extracting and isolating bio-based polymers such as cellulose and proteins fromrenewable feedstocks and waste streams. We aim to develop sustainable processes to convert