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cloud computing (e.g. AWS, Azure, GCP, etc). Experience with agile software development. Knowledge of research process, data lifecycle, and data workflows. Experience developing automated processes
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14850, United States of America [map ] Subject Areas: AI/Machine Learning Computer Science / AI + Software Engineering , AI/ML/DL Theory Material Science and Engineering Computational Science Physics
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, toxicology, or pharmacology Understanding of biological pathways and their relationship to disease mechanisms or drug response Experience with cloud computing environments and large-scale data processing
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; oversee selection, configuration, ruggedization, and lifecycle support of commercial data-acquisition systems for laboratory and field use. Program development and execution Develop technical roadmaps and
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implement cloud-native data pipelines connecting lab instruments, databases, and AI models Support model deployment, inference services, and experiment tracking (e.g., MLflow) Integrate LLM reasoning with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
Experience with version control systems such as GitHub, cloud- or cluster-based computing, systems management, statistical programming or mechanistic modeling, interactive data visualization (e.g., dashboards
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. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and verbal communication skills for interacting with colleagues
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diseases. Additional details about the lab can be found at http://ismagilovlab.caltech.edu/ Essential Job Duties Assist graduate students, postdocs, and research staff with bioinformatics/computational
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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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, nanoparticle arrays, and electrochemical catalyst systems, ensuring reproducible and contamination-free data. Link real-time TEM data streams to digital twin and AI platforms, using cloud-based computation