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17 Dec 2025 Job Information Organisation/Company Johannes Kepler University Department Institute for Machine Learning Research Field Computer science Researcher Profile Recognised Researcher (R2
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2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Job vacancy starting: 01.02.2026 (MM-DD
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in the analysis of nonlinear time-dependent PDEs and Operator Theory/Spectral Theory. Additional expertise in rigorous computer assisted methods (e.g. interval arithmetic) is a plus. Required
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Experience and practical knowledge of programming languages and tools (e.g. Python, Java, etc.) Knowledge in Software Engineering, AI, machine learning is an advantage Experience with software observability
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comparable qualification) in a relevant discipline (computer science, mathematics, AI) Expertise in one or multiple of the following areas: Deep Learning, Computer Vision, Signal Processing (Synthetic Aperture
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 7 days ago
the Medical University of Vienna, the Technical University of Vienna and University of Vienna, the AITHYRA and CeMM PhD programs for both life scientists and computational scientists/machine learning experts
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Hagenberg (50%). Job Duties: Independent research in symbolic artificial intelligence, computer algebra, computational logic, or automated reasoning with connection to machine learning Participation in
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equivalent fields of study. Position 1: In-depth knowledge in the areas of Biomedical Visualization, Biomechanics, Machine Learning, Development of Server/Client Applikationen, Daten Management. Position 2: In
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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve