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teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re passionate about groundbreaking international research and
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research findings. Analyse and interpret research results and findings from the project, using automatic methods with appropriate software. Supervise and teach other members of the research team (e.g. Master
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, challenging project. Learning on the job isn't just a benefit – it's a must. Education, Qualifications and Experience Essential Criteria Applicants should hold a PhD in a relevant area of Engineering
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51 Faculty of Physics Startdate: 01.09.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.08.2028 Reference no.: 4121 Explore and teach
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Learning, Algorithms, Noise Handling (Error Correction/Mitigation), and Verification. These roles are part of the Quantum Software Lab (QSL, link: https://www.quantumsoftwarelab.com ), in collaboration with
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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34 Faculty of Law Startdate: 15.09.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 14.09.2031 Reference no.: 4336 Explore and teach
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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing
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). The successful candidate will work with the wider team in the Struwe Lab, contribute to the development of new research areas, present their research findings at international conferences and learn advanced
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
for isomerization in MOFs: https://doi.org/10.1039/D3QI01065A What we are looking for: A PhD in Chemistry, Materials Science, Physics or a related field Experience in python (ASE, scikit-learn, pytorch) is essential