48 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" PhD scholarships at Technical University of Munich in Germany
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biological techniques (SSR, GC-EAD, EAG). •Experience in analytical chemistry (GC-FID, GC-MS). •Experience in or willingness to learn statistical data analyses, data processing and analytical chemical analyses
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, designed for acidic water-splitting reactions in polymer electrolyte membrane (PEM) units (e.g., https://onlinelibrary.wiley.com/doi/full/10.1002/aenm.202301450). Your tasks in detail: Collaborate closely
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processing parameters. You will develop machine learning models to analyse experimental datasets and uncover structure-function relationships that determine membrane performance. By combining statistical
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University of Munich (TUM), you will be submitting personal data. Please refer to our data protection information in accordance with Art. 13 of the General Data Protection Regulation (DSGVO) http://go.tum.de
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focus is developing and characterizing metallic high-performance materials for/through additive technologies using experiments and computer-aided methods. Furthermore, the chair is dedicated
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the course of the application process pursuant to Art. 13 of the General Data Protection Regulation of the European Union (GDPR) at https://portal.mytum.de/kompass/datenschutz/Bewerbung/. By submitting your
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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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research and travel budget available to best support your research. You will partic-ipate in teaching and supervising students, interact with and learn from the other team members, and re-ceive close
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5
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05.04.2023, Academic staff We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated learning