Post-Doctoral Research Associate: Computational Materials Science and Machine Learning - UTK

This position involves computational research on nuclear materials, with an emphasis on f-elements (lanthanides and actinides), molten salts, interfacial science, and energy-related processes within a multi-scale modeling framework. The main task focuses on developing and using machine learning interatomic potentials, advanced free energy simulations, and high-performance computing to predict chemical and materials properties, such as thermochemical behaviors and interfacial interactions relevant to nuclear applications.

Duties include technical interactions with undergraduate and graduate students, contributing to and developing new research initiatives, writing peer-reviewed scientific journal articles, and technical management of research projects.

The appointment would be a limited term appointment, approximately 1 year with the possibility of renewal for future years. The end goal after the appointment would be for you to secure a permanent position that is compatible with your interest.

• Development and evaluation of statistical and machine learning tools for designing and understanding functional materials using the leadership class high performance computing facilities.

• Work with multi-disciplinary teams to apply modeling and simulation techniques to bulk materials and interfaces, supporting both fundamental research and applied programs.

• Publishing papers in high-quality refereed journals.

• Actively collaborating with industry, academia, government labs, and applications developers in a variety of venues.

Required Qualifications:

• Position requires a Ph.D. degree in Materials Science, Chemical Engineering, Computational Chemistry, or an equivalent field.

• A well-established track record of research in materials science/computational modeling/chemistry.

• Experience in employing advanced statistical mechanics methods and machine learning for simulations of materials.

Preferred Qualifications:

• Programming experience using Python for workflow development, scientific computing, and data analysis and visualization.

• Demonstrated knowledge and experience in advanced free energy simulations methods, such as thermodynamic integration, metadynamics, and umbrella sampling.

• Experience in developing and utilization of machine learning interatomic potentials to enhance predictive modeling of chemical processes and materials.

• Demonstrated experience in modeling solid-liquid interfaces.

• Excellent interpersonal skills, oral and written communication skills.

• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.