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    Assistant Research Specialist - Upton, United States - Brookhaven Science Associates

    Brookhaven Science Associates
    Brookhaven Science Associates Upton, United States

    2 weeks ago

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    Description

    The Machine Learning Group within Brookhaven National Laboratory's Computational Science Initiative (CSI) invites exceptional candidates to apply for a Research Specialist (RS1/RS2) position focused on machine learning (ML).

    This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse scientific and security problems of interest to Brookhaven Lab and the Department of Energy (DOE).

    Topics of particular interest include novel development of deep learning ML models and adaptation of existing ones for scientific and security applications; ML models for natural language processing (NLP) and computer vision (CV), e.g., Large Language Models (LLMs) and multi-modal, multitask Foundation Models; and techniques supporting stakeholders and end users of applied ML methods, such as uncertainty quantification (UQ), interpretability and explainability for artificial intelligence (XAI), and responsible AI.


    This position provides access to world-class computing and data resources, such as Brookhaven Lab's Institutional Cluster and DOE leadership computing facilities.

    These platforms afford computing at scale, as well as access to unique data sources, ensuring the successful candidate will have the necessary tools to tackle challenging DOE problems of interest.

    In addition, the successful candidate will join a growing research group with diverse expertise and projects that span laboratory-level and nationally concerned DOE missions.

    This research staff position presents a distinct opportunity to conduct interdisciplinary collaborative research at Brookhaven Lab with a highly competitive salary.


    Position Description:

    Essential Duties and Responsibilities:

    • Assist in developing novel algorithms and conduct computational experiments.
    • Support dissemination of research findings in publications, posters, project reports, and presentations.
    • Effectively communicate with supervisor and team members.
    • May independently develop solutions and ideas.

    Position Requirements:

    Required Knowledge, Skills, and Abilities:

    • Bachelor's degree in computer science or a related field (e.g., engineering, applied mathematics, statistics, physics) with two or more (2+) years of related experience or a Master's degree in computer science or a related field (e.g., engineering, applied mathematics, statistics, physics) with one or more (1+) years of related experience in computational science or related fields (e.g., applied mathematics, statistics, physics).
    • Solid programming skills and computer science knowledge.
    • Familiar with deep learning libraries.
    • Proficient written and verbal communication skills.

    Preferred Knowledge, Skills, and Abilities:

    • Bachelor's degree in computer science or a related field (e.g., engineering, applied mathematics, statistics, physics) with five or more (5+) years of related experience or Master's degree in computer science or a related field (e.g., engineering, applied mathematics, statistics, physics) and three or more (3+) years of related experience or Doctorate in computer science or a related field (e.g., engineering, applied mathematics, statistics, physics).
    • Engineering or research experience with programming deep learning models.
    • Moderate publication track record in machine learning or related field.
    • Experience working as part of multidisciplinary scientific collaborations.
    • Familiar with Linux, Bash, Git, Docker, and/or other software development tools.
    Brookhaven National Laboratory is committed to providing fair, equitable and competitive compensation. This is a multi-level role and the full salary range for this position is $ $151700 / year. You will be placed at the level and salary commensurate with your experience.

    Salary offers will be commensurate with the final candidate's qualification, education and experience and considered with the internal peer group.

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