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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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. Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud). Understanding of BMS architecture and electric mobility systems.
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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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. Access an analytical ecosystem; cloud compute, engineering support, wet-lab validation pipelines, and established multi-omics workflows, allowing postdocs to focus on conceptual and scientific innovation
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applications of AI/ML in embedded or IoT devices. Knowledge of cloud-based computing platforms for data processing (e.g., AWS, Google Cloud) Understanding of BMS architecture and electric mobility systems.
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yield optimization. Exposure to quantum chemistry (DFT) and molecular simulations is a plus. Experience with cloud computing and/or high-performance computing (HPC) resources. Application Process