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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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the theoretical developments in computation argumentation (CA), information retrieval (IR), and natural language processing (NLP) using recent advances in large language models (LLMs). The candidate is expected
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aims to develop data-efficient and reliable training strategies for vision foundation models, reducing the need for large datasets and improving robustness. The project will explore strategies related
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E-Sports are competitive sports in virtual environments, where participants compete at amateur and professional tournaments, as well as socially, with top competitions attracting large audiences and
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) for analyzing large data sets Place of work: Umeå Forms for funding or employment: Employment as PhD student (4 years of education) Starting date: The preferred starting date is 1 May 2026, but flexible according
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for these architectures remains a key challenge. This project will explore how reconfigurable/spatial computing devices can be integrated into large-scale heterogeneous infrastructures, which include CPUs, GPUs, DPUs, and
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layers, using transport and optical techniques to measure the relevant properties. They will have the opportunity to work with two large-scale facilities which the research division is responsible
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genetics and data from real-world forestry to better understand how trees grow and adapt. It is perfect for you who want to combine hands-on field sampling, lab-based DNA work and big-data analysis. WIFORCE
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of the North Sea maritime infrastructure through AI-enabled, human-centered, and data-driven cyber-physical threat intelligence. This large interdisciplinary project builds on insights from network
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able to work with very large data and have the creativity and curiosity to work with network measures and statistics. You may also be an expert on grounded computational social science: combining large