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Field
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support to meet individual user and program needs. The individual will independently collaborate with the IT partners on Campus and School of Medicine to fulfill the technical mission of the NCIBT
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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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administration. Experience with one or more of the following technologies: Automation User/Group Management Storage Systems GPU programming and GPU support Networking Preferred Qualifications: High Performance
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implementing STIG processes and remediations Experience with GPU, SLURM and other HPC environments. Experience with CI/CD support and container administration. Deep familiarity with Red Hat Linux operating
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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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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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Node consists of five academics and ten PhD students. Our research is supported by a host of experimental facilities and access to state-of-the-art CPU and GPU computing clusters. Experimental facilities
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group and develop an innovative and impactful research program, which includes, but is not limited to, population and evolutionary genetics, metagenomics, eDNA-based monitoring, and phylogenomics
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sitting within a leading programme of clinical, health and bioinformatics at the South London and Maudsley (SLaM) Biomedical Research Centre (BRC) and forms a key component of both the Centre
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and a new GPU cluster. Opportunities for professional growth and career advancement. Collaborative and inclusive work environment that fosters creativity and innovation. Application of Domain Expertise