Argonne National Laboratory, a U.S. Department of Energy National Laboratory located near Chicago, Illinois, has an opening for a Postdoctoral Appointee specialized in deep learning applications for hydrology and hydrodynamic modeling, especially physics-informed machine learning at the Department of Hydrology, Environmental Science Division. The Postdoctoral Appointee will work toward advancing state-of-the art physics-informed artificial intelligence and machine learning (AI/ML) models to improve hydrologic systems modeling and near real-time forecasting at a high resolution. The project aims to develop a framework and standardized benchmark suite for scalable and robust physics-informed AI/ML for next-generation hydrologic and hydrodynamic modeling. The appointee will join a group of scientists working on this project supported by Argonne’s Center for Climate Resilience and Decision Science (CCRDS). Model development will utilize the leading high performance computing platforms at ALCF ( https://www.alcf.anl.gov ) and other Department of Energy computing platforms. This is a two-year position that we want to fill immediately. This position description documents the general nature and level of work but is not intended to be a comprehensive list of all activities, duties and responsibilities required of job incumbent. Consequently, job incumbent may be required to perform other duties as assigned. **Position Requirements** + Completed PhD within the last 0-5 years in hydrology, civil engineering or a related field + Knowledge of key approaches for embedding physics in AI/ML models, especially neural operators, physics-informed neural networks, hybrid modeling, and regularization techniques + Experience with various neural network architectures (e.g., graph neural networks, autoencoders, generative adversarial networks, etc.) + Understanding of hydrologic and hydrodynamic processes and modeling + Experience in applying AI/ML for hydrologic and hydrodynamic predictions + Experience in using AI/ML frameworks (e.g., PyTorch, TensorFlow, Flux, or similar) on HPC systems + Skilled in data analysis, statistics, and visualization, especially on large datasets + Knowledge of developing flood observation training datasets from multiple sources + Experience writing and modifying scientific code in Python, Julia, Fortran, and C++ Effective written and oral communication skills + Strong organizational skills and the ability to coordinate across a broad spectrum of activities + Demonstrated ability to work independently and in a team environment + Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork **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._