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ELLIIT collaboration, in which BTH leads computational and applied AI development. Research focus The PhD project will focus on the development of scalable and efficient machine learning approaches
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to learn innovative approaches in data analysis, including the programming, AI – tools, machine learning Mobility condition: - the candidate MUST NOT have their main activity (residence/work/studies) in
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language models, with hands-on experience in training, fine-tuning, or evaluating state-of-the-art models Solid knowledge in adversarial machine learning or trustworthy AI, including experience with
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market, Role of EVs in the grid, Power System Stability Analysis Using Machine Learning Techniques and more. Eligibility Requirements: Applicants must be Australian citizens or Permanent Residents
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learning for interconnected systems (e.g., 6G and Edge AI platforms, self-driving vehicle vision) in collaboration with industry partners and domain experts. This PhD thesis is offered in the context
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, including the programming, AI – tools, machine learning Residence outside the Czech Republic Nice-to-have: Prior experience with microscopy and plant research Experience living or visiting Czech Republic and
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(DInSAR). Minute surface uplift and subsidence signals will be automatically detected using machine-learning workflows, enabling systematic, user-independent identification of drainage events every 6–12
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advertisement Job description Integreat - the Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo invites applications for a doctoral research fellowship. The PhD candidate will work
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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PixHawk Autopilot, Arduino boards, Raspberry Pi - or equivalent Experience with ROS/ROS2 Experience with programming languages like Matlab, Python, C++ Familiarity with machine learning and/or deep learning