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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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at the intersection of artificial intelligence and cultural heritage. The successful candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or other related department to apply. The successful applicants will design controllers for a variety of
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exploring new modes of human-computer interactions. Has demonstrated experience in exhibiting works and/or presenting at festivals and conferences, with the ability to teach and support students in developing
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appreciated. The selected candidate will work on cutting-edge technologies in an excellent research environment, with a potential to work with a Quantum Computer through our collaboration partners, as
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architectures, diffusion models, and autoregressive techniques, as well as their applications in natural language processing, computer vision, and beyond. The course emphasizes hands-on learning, enabling
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policymakers Qualifications This position requires a PhD in Software Engineering, Computer Science, Computer Engineering, Information Systems, or a closely related field. Technical Expertise in one or more of
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, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications include: expertise in programming and coding (preferably using Python and C++) and GUI development
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deadlines. A bachelor’s degree in Computer Science or Computer Engineering is required. The candidate should also demonstrate experience and excellence in systems or tool building, algorithmic thinking, and