190 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "FORTH" positions at Technical University of Munich
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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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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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subjects, high interdisciplinary desire to learn, and willingness to cooperate, openness for internationalization and diversity, very good verbal and written English communication skills (good command
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forth with your own ideas on how to simulate the process of additive manufacturing. • You will publish research findings and participate in national and international conferences. Your Qualifications
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centers in Germany and around the world. The researcher taking on this position will be required to teach 5 hours per week, in accordance with postdoctoral workloads across Germany. The researcher will have
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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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, 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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Imaging, Machine Learning, or a related field • Demonstrated research experience in generative models for medical imaging (e.g., diffusion models, VAEs, GANs) • Publications in high-ranking journals and
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are available in early 2025 and will remain open until filled. Further information: https://www.mls.ls.tum.de/en/plasysbio/home/ Selected recent publications Graf, A., Bassukas, A.E.L., Xiao, Y., Barbosa, I.C.R
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understanding basic mechanisms governing gene expression to unraveling genetic aberrations triggering rare diseases and cancer. More at https://www.cs.cit.tum.de/cmm/home/. The project EPIC is a 10-million Euro