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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
the research. As a member of GEAR Center, the postdoctoral associate will be considered a Hock Tan Fellow. REQUIRED: Ph.D. in computer science or related technical or engineering field; experience with Python
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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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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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Proficiency with HPC and Cloud Computing environments, including distributed training (e.g. torchrun, slurm, deepspeed, etc.) Excellent communication and teamwork skills PREFERRED QUALIFICATIONS: Familiarity
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particle physics, computer science or a related field Experience with large-scale data management using grid or cloud technologies Experience in one of the on-site experiment would be an asset Familiarity
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Computer science Engineering Chemistry Researcher Profile Recognised Researcher (R2) Application Deadline 2 Mar 2026 - 22:59 (UTC) Country Finland Type of Contract Temporary Job Status Full-time Is the job funded
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with metabolomics workflows (LC-MS, GC-MS) and integrative multi-omics analysis. Knowledge of statistical modeling and systems biology approaches. Experience with HPC or cloud computing environments
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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