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(fastest execution) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations
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the mission areas of the Cancer Program and OSUWMC. This position will interface with all levels of the organization and will lead and facilitate cross functional teams to improve processes. Additionally
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Range $81,640-138,798/year Type of Position Staff Position Time Status Full-Time Required Education BS Click here for more information about equivalencies: https://hr.uky.edu/employment/working-uk
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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experience including optimizing clinic flow, providing in and outreach to patients and working collaboratively with the team to create a patient centered environment. S/he participates in Performance
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19 Jan 2026 Job Information Organisation/Company ERATOSTHENES CENTRE OF EXCELLENCE Department Big Earth Data Analytics Research Field Computer science Engineering » Computer engineering Researcher
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OPEN APULIA Patti Territoriali per il sistema universitario pugliese 2022 | Italy | about 1 month ago
Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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, implementation, maintenance, and optimization of our database systems. The role requires hands-on expertise in database management, performance tuning, backup/recovery strategies, and troubleshooting complex
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gas analysis, a GC/MS, and an HPLC/MS. DFT calculations will be performed using annual allocations on national high-performance computing centers. More details here: https://iramis.cea.fr/en/nimbe/lcmce
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, in collaboration with researchers in the computational field; d) Contribution to the development, validation, and optimization of predictive models (Random Forest, Gradient Boosting, Neural Networks