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. Experience with heterogeneous and parallel computing technologies, programming models, or accelerators, including CPUs, GPUs, FPGAs, and emerging computing technologies. Familiarity with quantum software
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, microfluidics, laboratory automation, and GPU computing infrastructure. The opportunity to develop AI methods and scientific software that are directly deployed on cutting-edge experimental platforms. Vacation
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machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure). Additional Qualifications: Experience with multi-GPU model training and
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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23 Jul 2026 Job Information Organisation/Company Toyota Technological Institute Research Field Technology Information science Engineering Computer science Researcher Profile Recognised Researcher
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7 Jul 2026 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Programming Computer science » Other Researcher Profile Recognised Researcher (R2
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pretraining, representation learning, or model evaluation. Experience with PyTorch and the Hugging Face ecosystem. Experience with high-performance computing, SLURM, distributed multi-GPU training, or large
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. Applicants are expected to have strong programming skills in Python, hands-on experience with PyTorch, and practical experience with GPU computing. Experience with engineering simulation, computational
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the ELLIS network of excellence in AI. You will be embedded in a vibrant, international team of PhD students and postdoctoral researchers, with access to substantial GPU compute, real robots and rich
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
(health insurance, paid leave, restaurant subsidy, etc.). Access to Inria’s computing infrastructure and to the ĀnandaBot project’s dedicated hardware (GPU servers, robot platforms). Integration in a well