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degree of teamwork ability Admission Requirements Completed Master in the field of Computer Science, Software Engineering and Management, Information and Computer Engineering, Biomedical Engineering, or
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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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analyses, data science, and machine learning Execution of case studies and energy-economic analyses Preparation of project presentations and reports; scientific publications in national and international
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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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measure gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning
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gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning, quantum computing
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administrative tasks Training and further education Your profile: Degree or Master's degree in glaciology, meteorology, geosciences, computer science, electrical engineering, physics or related fields Motivation
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working practice, and strong team working skills. Admission Requirements Master or Diploma degree in Computer Science, Computer and Information Engineering, Software Development, or equivalent fields
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 3 months ago
, startups and patient partners. Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and