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and HPC/GPU systems, with version control (git) and reproducible workflows (conda or containers, Snakemake or Nextflow).•Able to work independently as well as within an interdisciplinary, international
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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selection criteria Good oral and written presentation skills in Norwegian, or another scandinavian language Experience with the CUDA programming model for general-purpose GPUs Experience with the OpenCL
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datasets, high-end GPU and storage infrastructure, international research collaborations, and dedicated funding for international conference participation. Supervision The PhD candidate will be supervised by
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responsible for: Conducting research on sustainable and resource-efficient AI systems for edge datacentres. Creating hardware-aware search spaces for CPUs, GPUs, and accelerators, and developing multi-objective
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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, optimization, or high-performance computing is highly desirable Experience with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
, expertise, and powerful computational resources (GPUs & CPUs) of the team. This PhD position is fully funded within the framework of RoGSiLT: Robust and Generalizable Sign Language Translation , a