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, including MPI, OpenMP, GPU programming or distributed computing. Scientific workflow systems and multi-physics coupling. IMAS, including IDSs, the Data Dictionary and Access Layer. MUSCLE3 or similar workflow
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computing needs, including GPU-enabled workstations, specialized software, reproducibility, performance, and secure remote access. Ability to diagnose issues across operating systems, drivers, remote desktop
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conduct world-class applied research. We change and make a difference. Do you want to become one of us? TheDepartment of Computer Science (DIDA) is one of two departments in the Faculty of Computing. It is
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Faculty.Instructor.Adjunct Electrical and Computer Engineering - Pennsylvania-Pittsburgh - (26005016) Qualifications: Ph.D. in Electrical Engineering with expertise in electrical power systems ECE
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Faculty.Instructor.Adjunct Electrical and Computer Engineering - Pennsylvania-Pittsburgh - (26005055) Qualifications: Ph.D. in Electrical Engineering with expertise in electrical power systems ECE
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are looking to fill a position as Scientific Programmer (m/f/d) High-Performance Computing and Software Engineering The department investigates the dynamics of the ocean carbon cycle, its variability and
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the thermal process from GPU computation to radiative cooling Integrate the algorithms into a larger computational satellite simulation environment Identify new cyber-physical systems research topics related
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to gather requirements, manage expectations, and balance competing priorities. Experience supporting high-performance or GPU-enabled scientific computing environments, including NVIDIA/CUDA, performance
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architectures. Emphasis will be placed on heterogeneous computing and the optimal use of CPU and GPU resources to maximize model performance, scalability, and portability. A particular focus of the position will
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education or research environment 3+ years of relevant professional experience Experience in GPU computing Experience with Github for software management Experience with the usage of workflow languages (like