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                Field
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                Theoretical Physics or a related discipline completed within the last 5 years. Experience with High Performance Computing and programming for massively parallel computers. Experience with quantum many-body 
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                for the leadership and management of the EPSRC programme grant: Advancing Parallel Mesh Generation and Geometry Representation to Enable Industrially Relevant, High-Fidelity Simulations (REMODEL). The REMODEL project 
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                other research services and resources. Strong programming skills in languages such as C, C++, Fortran, Python, or other languages commonly used in HPC environments. Deep understanding of parallel 
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                algorithm development and application. Familiarity with common scientific programming languages such as C++. Experience in parallel programming with one or more common parallel programming models, like MPI 
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                designed for cutting-edge engineering applications. Additional requirements include proficiency with C++ and parallel programming models, experience developing complex simulation software as part of a 
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                computational, molecular and biophysical techniques. During the project you will design the methodology for, and perform the directed evolution of H-transfer enzymes using parallel selection methods, measure 
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                Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Department of organic chemistry (Faculty of Science) at Charles 
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                , or similar). Experience with parallel programming frameworks (e.g., MPI, OpenMP, CUDA) and high-performance interconnects (e.g., InfiniBand). Preferred Qualifications: Familiarity with advanced storage 
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                We are seeking a Sr. Research Data Analyst who will build complex data models and conduct complex data analysis in support of research projects, programs, and/or studies. The Sr. Research Data 
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                programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA