Postdoc in AI-Driven Materials Discovery

## Job Title Postdoc in AI-Driven Materials Discovery ## About Us Join us at the Division of Chemical Physics, Department of Physics and Astronomy, and contribute to the development of next-generation artificial intelligence methods for materials discovery and sustainable energy technologies. The Division of Chemical Physics conducts internationally recognized research at the interface of physics, chemistry, materials science, and data science. Our research spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions between faculty, postdoctoral researchers, PhD students, and external partners from academia and industry. The Department of Physics and Astronomy is one of the largest departments at Chalmers University of Technology, with broad activities ranging from fundamental physics to applied research addressing societal challenges in energy, sustainability, and digitalization. ## About the Research Project The project aims to develop novel AI and machine-learning methods for accelerated materials discovery. The focus is on combining generative AI, active learning, first-principles simulations, and machine-learning potentials to identify new multicomponent optoelectronic materials for energy-conversion applications. The successful candidate will contribute to the development of an autonomous materials-discovery framework capable of navigating extremely large compositional spaces while simultaneously optimizing multiple target properties such as optical performance, stability, and sustainability. ## Responsibilities As a postdoctoral researcher, you will: * Develop machine-learning and AI methods for materials discovery. * Design and implement active-learning workflows for autonomous exploration of materials spaces. * Develop and evaluate generative AI models for inverse materials design. * Perform large-scale computational screening using first-principles calculations and machine-learning potentials. * Analyze structure–property relationships and extract scientific insights from AI models. * Publish research results in leading international journals and present findings at international conferences. * Contribute to the development of open and reproducible scientific software. The position also includes: * Supervision of master's students and, to some extent, PhD students. * Opportunities to contribute to teaching at undergraduate and master's levels. * Collaboration with national and international academic and industrial partners. ## Requirements **Mandatory Qualifications** * A doctoral degree in Physics, Materials Science, Chemistry, Chemical Engineering, Computer Science, Applied Mathematics, or a closely related field, or an equivalent foreign degree. The degree must be awarded no later than the time the employment decision is made. * Strong written and verbal communication skills in English. * Experience in machine learning, scientific computing, computational physics, computational chemistry, or related fields. * Experience with Python programming and modern scientific software development. * Ability to work independently while contributing effectively to collaborative research projects. * Strong analytical and problem-solving skills. * Good interpersonal skills and a demonstrated ability to collaborate in interdisciplinary teams. * Expected to have some experience with teaching or supervision and to demonstrate strong potential for future development in both research and education. ## Nice to Have **Meritorious Qualifications** * It is highly meritorious if the doctoral degree has been obtained within the last three years prior to the application deadline. * Experience with machine learning for scientific applications. * Experience with deep learning frameworks such as PyTorch or TensorFlow. * Experience with atomistic simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. * Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization, or reinforcement learning. * Experience with high-performance computing (HPC). * Experience supervising students or junior researchers. ## Benefits * Temporary full-time employment for two years with the possibility of a one-year extension. * Employee benefits including generous parental leave, subsidized day care, free schools, and healthcare. * Dynamic and inspiring working environment in the coastal city of Gothenburg. * Swedish courses offered for non-native speakers. * Commitment to gender balance, equality, and inclusion. ## Application Procedure The application should be written in English and attached as PDF-files (maximum size 40 MB per file; Zip files not supported). **Required Documents:** * **CV:** A comprehensive CV, including a complete list of publications and details of previous teaching and pedagogical experience. * **Personal Letter:** A brief introduction about yourself, a summary of your previous research fields and key research outcomes, and an outline of your future goals and research focus. A background check may be conducted as part of the process.