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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 10 days ago
. These simulations are run on highly parallel supercomputers on which both the hardware and the software are optimized for the task at hand. While the computing power of each processing unit is still increasing, the
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protocols that integrate simultaneous measurements of behaviour, metabolism and stress hormones. Complementing the parallel PhD project (Position One), this position focuses on cod and includes
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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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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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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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Application deadline: 31/01/2026 Research theme: Control Engineering, Robotics UK only This 3.5-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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: Recent PhD and/or MD in a relevant field, or equivalent research experience, with a background in molecular biology, transcriptional regulation, functional genomics, computational biology, genetics
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LevelPhD or equivalent Skills/Qualifications Who you are You have a PhD, preferable in Computer Science, Engineering, Mathematics, or equivalent. You have a strong background with parallel and distributed