Are you passionate about harnessing the power of supercomputers and artificial intelligence to revolutionize scientific workflows for computational chemistry and material science? Join us at Argonne National Laboratory, where we are committed to accelerating major scientific discoveries and engineering breakthroughs. We are seeking a forward-thinking postdoctoral appointee to explore and implement large language model (LLM) agents for streamlining complex workflows, making them more efficient and more accessible. This role offers a unique opportunity to work on innovative projects, coupling state-of-the-art machine learning algorithms with traditional simulations. You will develop interatomic machine learning potentials with high-accuracy quantum chemistry methods and evaluate LLMs based on their performance in assisting complex scientific tasks. Core Responsibilities: + Apply LLMs to streamline and manage complex scientific workflows, enhancing user accessibility and efficiency. + Develop and implement innovative solutions using LLMs to support ANL's mission of accelerating scientific discovery. + Conduct research and contribute to publications, presentations, and reports, promoting the integration of AI in computational chemistry and material science. + Collaborate with interdisciplinary teams and external partners to maximize the impact of the methods developed. **Position Requirements** Required skills and experience: + Recently completed Ph.D. (typically within the last 0-5 years, or to be awarded in 2024) in Physics, Chemistry, Materials Science, or a closely related field. + Strong programming skills in Python, or other relevant languages used in scientific computing. + Experience with machine learning techniques, including training, validating, and implementing models. + Experience with high-performance computing environments and tools. + Strong interest in large language models and their application in scientific domains. + Understanding of quantum chemistry methods and experience in applications of these methods. + Ability to conduct independent research and demonstrated publication record in peer-reviewed journals. + Innovative thinking and problem-solving skills in tackling complex scientific challenges. + Interest in contributing to open-source projects and community-driven initiatives within computational science. + Effective communication skills for effective collaboration with interdisciplinary teams and clear presentation of complex technical information. + Ability to model Argonne's core values of impact, respect, safety, integrity, and teamwork. Desired skills and experience: + Experience in large language models and their applications in scientific domains. + Knowledge of workflow tools and automation techniques to enhance efficiency in complex computational tasks. + Experience in developing and using machine learning potentials for molecular dynamics simulations. + Experience with AI frameworks and technologies relevant to computational chemistry and materials science. **Job Family** Postdoctoral Family **Job Profile** Postdoctoral Appointee **Worker Type** Long-Term (Fixed Term) **Time Type** Full time _As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law._ _Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department._ _All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment._