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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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humans and society at large is either fully automated or heavily relies on automatically provided decision support. While machine learning approaches become increasingly prevalent in this context
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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if you have worked with prediction models, machine learning or AI models and are familiar with blood cells such as neutrophils, leukocytes and platelets. Work experience in the area is meritorious. If you
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. To meet the general entry requirements for doctoral studies, you must: Hold a Master’s degree in computer science, image analysis and machine learning, engineering, data sciences, applied mathematics
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interdisciplinary, applied research with expertise in visualization, design, computer graphics, and the learning sciences. The research nexus for the division is the Visualization Center C, a unique science center in
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are looking for: To qualify as a PhD student, you must have a Master's degree (masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits in Machine Learning, AI, Data Science, Computer
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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: We are looking for a dedicated candidate who is eager to learn new things and contribute valuable knowledge to society’s work on climate adaptation and the conservation of biodiversity. Applicants
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machine learning, engineering, data sciences, applied mathematics, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including