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well as command-line bioinformatics tools. Experience with high-performance computing (HPC) environments, including the use of job scheduling systems (e.g. Slurm) – considered an asset. Very good command of English
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, or reinforcement learning. Experience with high-performance computing (HPC). Experience supervising students or junior researchers. What you will do As a postdoctoral researcher, you will: Develop machine-learning
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agents. Familiarity with responsible AI principles, including fairness, transparency, and data governance. Proven experience with supercomputing / HPC environments. Strong academic writing and
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, Harmony, Monocle3, or scVelo Knowledge of immunology Experience with clinical datasets and HPC Strong written and verbal communication skills Ability to work independently and collaboratively Demonstrated
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
++, or similar. Preferred Qualifications, Competencies, and Experience Preferred qualifications include experience with molecular dynamics or atomistic simulations, supercomputing or HPC environments, scientific
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Proficiency in R or Python Minimum of two years of experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer
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: · Mammalian cell culture experience. · Experience with high-performance computing (HPC) environments. · Experience in UNIX and Python/R · Exposure to reverse genetics systems or viral
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., DAS, broadband networks). Proficiency in open-source, version-controlled environments (Python, git, conda). Comfort with cloud/HPC workflows in a Unix-based environment. Preferred Qualifications
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with HPC systems, machine learning, and GRB monitor data analysis would be an advantage. Additional Information Applications must be submitted electronically and include a cover letter, statement of
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific