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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new machine learning methods for multimodal data and
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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, contribute to a better world. We look forward to receiving your application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and
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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested developing new machine learning methods for precision medicine and
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to assimilate knowledge at the research level. Understanding and experience in machine learning and computer vision. Knowledge, experience, and strong interest and in AI and XR development. Knowledge and
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approaches that combine artificial intelligence, machine learning, natural language processing, and social sciences. This collaborative and cross-sectoral approach aims to produce robust methods for evaluating