Scientific Associate - Foundation Models for Scientific Exploration

The Center for Functional Nanomaterials (CFN) at Brookhaven is a DOE-funded national scientific user facility, offering users a supported research experience with top-caliber scientists and access to state-of-the- art instrumentation. The CFN mission is advancing nanoscience through frontier fundamental research and technique development and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users.

Three strategic nanoscience themes underlie the CFN scientific facilities: The CFN conducts research on nanomaterial synthesis by assembly designing precise architectures with targeted functionality by organizing nanoscale components. The CFN researches and applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes advanced capabilities for studies of Nanomaterials in Operando Conditions for characterizing materials and reactions at the atomic scale in real-world environments.

The CFN is seeking an exceptional researcher to pursue frontier research in artificial intelligence and machine learning (AI/ML) to advance scientific discovery. The CFN has a productive research program on accelerating material discovery and developing autonomous experimentation. In this 1-year term on-site position (renewal possible), you will be involved in the development and application of AI/ML methods for nanoscience experimentation, instrumentation, and ideation. You will research the application of frontier foundation models to scientific tasks, including leveraging large language models (LLMs), vision models, and multimodal models.

As the Scientific Associate, you will have these roles and responsibilities:

• You will be responsible for developing and deploying AI/ML methods for scientific exploration and experimentation.

• You will apply foundation models (LLM, vision, multimodal) to scientific contexts.

• You will collaborate with researchers to create practical tools for performing nanoscience research via natural language.

• You will produce software packages for the developed tools; disseminate new knowledge through publications and presentations.

You are qualified for this role if:

• You have a Bachelor's degree in computer science, physics, mathematics, engineering, or a related discipline; a Master's degree is preferred;

• You have experience-knowledge in research on AI/ML and software development;

• You have the ability to communicate effectively by writing scientific papers or giving technical presentations;

• You have a demonstrated ability to work in a group of researchers with diverse academic background;

• You are committed to fostering an environment of safe scientific work practices.

You are well-matched to this position if:

• You have a minimum of three (3) years progressively responsible related work experience including supporting scientific research programs following established methods and standards for investigation and experimentation;

• Master's degree preferred;

• You have recent experience with foundation models, embedding models, modern NLP/LLM systems, and chatbot systems;

• You have experience with modern software development systems, such as version control or continuous integration;

• You have basic familiarity with database systems.

Additional Information:

• Initial 1-year term appointment subject to renewal contingent on performance and funding.

• Appointment level will be commensurate with experience and qualifications.

• This is a fully onsite position located at BNL.

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 $67550 - $85000 / 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.