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Postdoc Researcher in Machine Learning

Position Description:

The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research associate position in machine learning (ML) and natural language processing (NLP). This position offers a unique opportunity to conduct research in emerging interdisciplinary research problems at the intersection of data science, machine learning, and natural language processing with applications in diverse scientific domains of interest to BNL and the Department of Energy (DOE).
Topics of specific interest include: (i) novel development and adaptation of existing ML and NLP models, for scientific and security applications; (ii) exploitation of large frozen models (e.g. large language models) to support or enhance application-specific tasks, including via development of helper models and integration with external resources; (iii) multi-modal and multi-task large ML models; and (iv) interpretability/explainability and visualization techniques for scientific ML applications.

The position includes access to world-class HPC resources, such as the BNL Institutional Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale and will ensure that the successful candidate will have the necessary resources to solve challenging DOE problems of interest.

This program provides full support for a period of two years at CSI with possible extension. Candidates must have received a doctorate (Ph.D.) in applied mathematics, statistics, computer science, or a related field (e.g., mathematics, engineering, operations research, physics) within the past five years. This post-doc position presents a unique chance to conduct interdisciplinary collaborative research in BNL programs with a highly competitive salary.

Essential Duties and Responsibilities:

  • Conduct research in various machine learning and natural language processing problems in the context of scientific discovery and workflow acceleration.
  • Implement and adapt ML and NLP algorithms for scientific and security applications.
  • Work in interdisciplinary collaborations with applied domain scientists on various aspects of scientific data generation, collection, curation and processing.
  • Formulate own high-quality research ideas and directions in collaboration with mentors in the group.
  • Communicate research progress, challenges and achievements, and engage within and beyond the group on new potential collaborations.

Position Requirements:

  • Ph.D. in computer science, statistics, applied mathematics, or a related field (e.g., operations research, physics, mathematics, engineering) awarded within the last 5 years
  • Programming experience in machine learning, scientific modeling, or scientific computing.
  • Practical experience in implementing machine learning algorithms on machine learning platforms (i.e. PyTorch, TensorFlow, JAX, DeepSpeed, etc)
  • Communications and interpersonal skills to interact effectively with a diverse group of scientists, engineers, and technical staff

Preferred Knowledge, Skills, and Abilities:
  • Practical experience developing novel scientific machine learning or natural language processing algorithms and models
  • Experience in scientific ML applied to domain sciences problems (e.g., in physical sciences, life sciences, or engineering)
  • Experience with large language models, prompt engineering, prompt learning (in data and feature space), and multi-modal large ML models
  • Experience with Explainable/Interpretable AI and data and model visualization techniques