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the emergence of edge computing, data storage will become more geo-distributed to account for performance or regulatory constraints. One challenge is to maintain an up-to-date view of available content in such a
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You will be working in a larger research and development project in parallel, distributed and heterogeneous computing. The project work can, under certain circumstances, be combined with an internal
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skills and qualifications: A recent PhD (completed within the last 5 years) in computer science, electrical engineering, or a related field. Strong background in network interconnect design and
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qualification, you must hold a PhD degree in computer science, software engineering, biomedical engineering, data science, or a similar field. Your project management skills include: Experience in technical
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Identify new applications for Machine Learning in science, engineering, and technology Develop, implement and refine ML techniques Implement parallel ML training on the High Performance Computers Engage in
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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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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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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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projects will work towards this goal. PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers