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on large and diverse datasets that include both genuine and synthetic voices allows these models to improve their accuracy and robustness. Apart from proposing and implementing deep learning methods
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. The successful candidate will employ qualitative and participatory research methods, including interviews, focus groups, and co-design approaches, to ensure that the perspectives of people with lived experience
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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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cosmolgy, galaxy evoltion and stellar astrophysics. Students in my group primarily perform numerical simulations of stars, in order to study broad questions related to the origin of the elements in
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Image processing and computer vision Experimental data analysis and uncertainty quantification Piezoelectric actuation, acoustic systems or electronic driver development Eligibility and Project
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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diverse data sources while addressing the challenges of limited computation, memory, and energy availability at the edge. Leveraging advances in multi-modal deep learning, sensor fusion strategies, and
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Statistical Methods, Automated Planning and/or Reinforcement Learning.
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark