12 parallel-and-distributed-computing-phd-"https:"-"Washington-University-in-St" Postdoctoral positions
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optimization in distributed systems. The work also involves modern compiler infrastructures, with emphasis on MLIR, and contributions to LLVM and the OpenMP standard. Applicants must hold a PhD in Computer
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commuting to the assigned work location when necessary. Qualifications We Require: PhD in engineering, physics, applied mathematics, computer science or other relevant field Ability to obtain and maintain a
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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to significant losses during processing, transport, retail and storage stages. In parallel, advances in sensor, information and communication technologies, together with increased computational capabilities, have
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engineering; Formal methods, models, and languages; Interactive and cognitive systems; Distributed systems, parallel computing, and networks. The successful candidate will work closely with teams specializing
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, or deployment at scale. A proven track record of high-quality research contributions published in top-tier machine learning conferences or journals. Proficiency in high-performance computing, distributed and
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based