Deputy Department Chair, Artificial Intelligence Department

Overview

Brookhaven National Laboratory (BNL) is seeking an exceptional leader and world‑class researcher to serve as the Deputy Department Chair for the Artificial Intelligence (AI) Department within the Computing and Data Sciences (CDS) Directorate. This is a pivotal leadership role designed for an individual who is passionate about shaping the future of AI for science and driving the department toward international preeminence.

The AI Department at BNL maintains a vibrant research portfolio that spans the spectrum from fundamental AI theory to the deployment of novel models within complex scientific workflows. As Deputy Chair, you will partner with the Department Chair to oversee strategic growth, foster interdisciplinary collaborations, and lead high‑impact research initiatives in areas such as Scientific Machine Learning (SciML) and Multimodal Scientific Foundation Models.

The Deputy Department Chair will report directly to the Chair of the AI Department and play a central role in managing the department's scientific mission and operational excellence. While providing executive‑level support to the Chair, the selected candidate will also lead research efforts that apply advanced AI/ML and data‑driven approaches to critical scientific domains, including biological research and large‑scale experimental data analysis.

You will work at the intersection of computational science and domain‑specific research, collaborating with a diverse team of AI researchers and scientists to translate complex scientific questions into transformative computational frameworks.

• Strategic Leadership:
  Assist the Department Chair in defining the research vision, identifying emerging funding opportunities, and scaling the department’s capabilities to the next level.
• Research Excellence:
  Conduct and oversee cutting‑edge research in artificial intelligence, machine learning, and data science, with a particular emphasis on scientific discovery.
• Interdisciplinary
  
  Collaboration:
  
  Build and maintain strong partnerships between AI researchers, computational scientists, and domain experts to integrate AI into experimental and theoretical workflows.
• Operational Oversight:
  Contribute to the development of reproducible analysis pipelines, research software, and the documentation of high‑impact results for peer‑reviewed publication.
• Mentorship and Growth:
  Help recruit, mentor, and retain a world‑class team of scientists and engineers, fostering a collaborative and inclusive research environment.

• Education:
  
  Ph.D. in Computer Science, Statistics, Applied Mathematics, Biology, or a closely related computational field.
• Expertise:
  Six (6) plus years of relevant work experience, a distinguished track record of expertise in AI/ML, statistical learning, or advanced data analytics. Experience effectively supervising staff.
• Innovation:
  Proven experience in developing, applying, or evaluating novel machine learning methods on complex, real‑world datasets.
• Collaboration:
  
  Demonstrated ability to work effectively in a collaborative, interdisciplinary research environment.
• Productivity: A strong track record of research productivity as evidenced by high‑impact peer‑reviewed publications or equivalent research outputs.

• Expertise:
  Ten (10) plus years of relevant work experience, a distinguished track record of expertise in AI/ML, statistical learning, or advanced data analytics. Experience effectively supervising staff.
• Strategic Funding & Proposal Leadership:
  Lead the technical narrative and budget planning for large‑scale funding opportunities, such as Department of Energy (DOE) missions or multidisciplinary research grants.
• Research Program Oversight:
  Manage the end‑to‑end lifecycle of department projects, ensuring technical milestones are met and results are translated into peer‑reviewed publications or project deliverables.
• Advanced AI System Engineering:
  Oversee the fine‑tuning, benchmarking, and deployment of Multimodal Scientific Foundation Models and Large Language Models (LLMs) within high‑performance computing…