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deployment (e.g., Docker) and cloud/serverless architectures (AWS SAM, Lambda, Amplify); experience with React or web and mobile application development; familiarity with GitHub; excellent project management
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
cloud/serverless architectures (AWS SAM, Lambda, Amplify); experience with React or web and mobile application development; familiarity with GitHub; excellent project management and communication skills
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with high-performance computing (HPC) environment, cloud-based computation, software tools for genetic epidemiologic analyses (e.g. GWAS, whole-genome sequencing, polygenic score, Mendelian Randomization
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sequencing data, whole-genome alignments, and assemblies is preferred. Candidates should be familiar with computational biology techniques and scientific programming. Prior experience with cloud and/or high
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data
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, scoring functions, predictive models or quantum chemistry Machine learning or AI frameworks applied to molecular discovery. Familiarity with cloud or high-performance computing environments. Experience
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of clinical and multi-omic data to uncover microbiome–host relationships -Experience with Python/R and cloud computing required Ideal candidate: A computational biologist or bioinformatician with strong
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). Knowledge of high-performance computing or cloud environments for large-scale data. Strong collaboration skills and ability to work in interdisciplinary teams. Special Requirements: Applicants cannot have
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computing environments (e.g., Linux, cloud computing, cluster management). FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40 Job FTE 1.0 Work Calendar Fiscal Job Category Research