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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PhD student will work in the SICAL team at LIRIS, recognised for its
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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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5 Feb 2026 Job Information Organisation/Company University of Bologna Research Field Computer science Researcher Profile First Stage Researcher (R1) Application Deadline 4 Aug 2026 - 23:00 (UTC
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Department. Profile You hold a master’s degree in Electrical Engineering, Computer Engineering, Telecommunications, Computer Science, or a closely related discipline, or you will have obtained it by the time
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science background, preferably in algorithm design, system programming, networking, operating system, and computer architecture. Experience in low-latency RPC, network stacks, distributed systems, host data plane
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event-based cameras. 2. Developing the first-ever AI/ML algorithm to predict the transition in real time. This will be implemented in benchmark transient multiphase flows, such as bubbly flows, turbulent
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. The investigator collaborates with interdisciplinary clinical teams to integrate these computational tools directly into hospital workflows. Routine duties include algorithm optimization and the evaluation of model
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of Health Informatics as part of a group of over 30 researchers using clinical data to improve our understanding of disease and the effectiveness of treatments, and implementing AI algorithms to deliver safer
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the context of critical illness. This position focuses on computational modeling of host-response mechanisms using high-dimensional multi-omics datasets. The fellow develops novel computational pipelines