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commitments. Integrate AI-enabled tools into recruiting processes-such as candidate sourcing, screening support, and job description optimization-where appropriate and validated, to improve efficiency
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scalable bioinformatics pipelines on cloud-based infrastructure. The Research Fellow will be responsible for the code base supporting the large-scale genomic processing and analysis pipelines at the SMaHT
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education Familiarity with distributed computation solutions Familiarity with GPU profiling and optimization Experience with data/storage systems Strong prioritization, process, and time management skills
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, Informatica, MuleSoft, Boomi, SnapLogic, or similar). Demonstrated strength in data modeling, master data management, metadata management, and performance-optimized design. The following Additional
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reading assignments; Works with faculty course heads to create/update group projects; updates labs and optimizes experiments; Creates/updates section materials to be used by teaching staff; Develops new
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will be discussed during the interview process. All remote work must be performed within one of the Harvard Registered Payroll States, which currently includes Massachusetts, Connecticut, Maine, New
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large-scale genomic processing and analysis pipelines at the SMaHT Data Analysis Center that manages multi-omic data (e.g., Illumina/PacBio/ONT Whole Genome Sequencing (WGS), RNA-Seq). The ideal candidate
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of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing
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program. The role supports the HGSE mission by ensuring that capital investments are strategically prioritized, effectively executed, and delivered in a manner that supports optimal teaching and learning
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objectives, and reading assignments; Works with Director of Laboratory Instructional Programs to update labs and optimize experiments Creates/updates section materials to be used by Teaching Staff; Experiments