36 learning-"https:" "https:" "https:" "https:" positions at Chalmers University of Technology
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, marine environment and shipping and look forward to perform interdisciplinary research. We also believe that you have a strong curiosity and a genuine wish to learn and develop your skills and knowledge
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devices and use them for your experiments. Contract terms The Doctoral student positions are fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up
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emerging non-terrestrial networks, including satellites, high-altitude platform stations, and unmanned aerial vehicles. Profile 2: Reliable and learning-enabled radio localization and sensing. This profile
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, marine environment and shipping and look forward to perform interdisciplinary research. We also believe that you have a strong curiosity and a genuine wish to learn and develop your skills and knowledge
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and machine learning, digital health, and advanced signal processing, with applications in healthcare, autonomous systems, industry, and energy. Through interdisciplinary research, we contribute
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of the central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding. The goal is to develop new
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, or a related field. It is an advantage if you have experience with probability theory, reinforcement learning, or Markov Decision Processes. Coursework or project experience in multi-agent systems
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experiments. Contract terms The Doctoral student positions are fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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, or a related field. It is an advantage if you have experience with probability theory, reinforcement learning, or Markov Decision Processes. Coursework or project experience in multi-agent systems