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Field
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at the realization of large-scale diffusion language models and flow models. Specifically, tasks include but are not limited to: training using parallel GPUs, improving diffusion language models and flow models
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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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welcome but not required. It is a plus if you additionally have hands-on robotics experience, experience with differentiable / GPU-parallel simulation (e.g. Isaac Lab, MuJoCo MJX, Genesis), or experience
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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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working on the LHCb experiment at the Large Hadron Collider. The successful candidate will focus on developing LHCb’s real-time data analysis systems, including the GPU-based software trigger. This work
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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
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, particularly oncology, genomics, imaging, or clinical NLP is a plus, but not required Experience with scalable ML infrastructure, multi-node GPU training, or local/private deployment settings We offer A full
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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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institutional clusters. Write robust Linux bash scripts and job submission scripts for SLURM and PBS environments, including multi-node GPU/CPU workflows, monitoring, restart, and post-processing pipelines
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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. 4. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints