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performance. The Project/Program Manager (Systems Engineer) focuses on the deployment, configuration, monitoring, and maintenance of GPU-enabled servers, virtualized environments, and hybrid infrastructure
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computing frameworks (e.g., MPI, NCCL) and model parallelism techniques. Proficiency in C++/CUDA programming for GPU acceleration. Experience in optimizing deep learning models for inference (e.g., using
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, advancing bioinformatics-driven research, facilitated by exceptional computational infrastructure including a centrally administered high-performance CPU and GPU cluster and network storage. This includes
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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free text of both biomedical literature and electronic patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling
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Description Primary Duties & Responsibilities: Lead the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and
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the optimization of large-scale LLMs and deep learning architectures for biomedical research. Design and deploy high-performance AI systems using GPUs and hardware accelerators. Interact and collaborate
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experience. PACU/Critical Care/GPU experience strongly preferred. Experience working with patients 6 weeks in age to 25 years of age preferred. Licensure/ Certifications: Current Massachusetts license as a
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software stacks, including GPU clusters and related computing resources. This position will also play a key role in designing and implementing new features for research projects. The successful candidate
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and model generation, point cloud rendering, visual effects (GPU shader, shadergraph, VFX) and 3D scene design Development of AR/VR applications What you bring to the table Full-time student at a German