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frameworks (e.g. PyTorch, TensorFlow) and relevant libraries. Practical experience in scalable data processing, including the use of parallel computing, cloud platforms, and distributed systems for efficient
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meet security, compliance, and integration requirements. You will collaborate with various professionals and act as the link between technical teams, clinical teams, and project management. In parallel
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exploring them. Basic data preprocessing, feature engineering, and model evaluation, or a strong willingness to gain hands-on experience. Eagerness to learn HPC concepts, including parallel computing
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clusters, including CPU and GPU architectures; Proficiency with job schedulers (e.g., Slurm); Knowledge of parallel and distributed computing principles; Understanding of data security and compliance
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hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied
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with the architecture and performance characteristics of distributed computing and data handling systems. Extensive knowledge in computer science or related field, demonstrated through education or
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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based on MPI. Experience working with the architecture and performance characteristics of distributed computing and data handling systems. Extensive knowledge in computer science or related field
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working with high performance computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to work independently and
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laboratory at LHO. In parallel, the chosen candidate will assist in forging results of this R&D into a conceptual reference design, construction plan, and parametric cost estimate for CE. These will form