PhD Position: Computational Discovery of Advanced Battery Materials

Sodium-ion batteries hold great promise to offer a viable solution for reducing dependence on critical materials and developing battery technologies with improved circularity. The NANEXBAT research consortium was granted by the Dutsch Research Council (NWO) to explore in the next 5 years materials innovations for next-generation sodium-ion batteries (NIBs).Understanding and controlling the evolution of materials across multiple length and time scales (from atomic composition and structure to functional components and complex devices) is essential for designing the next generation of high-performance energy systems. By integrating physics-based multiscale modeling with machine learning (ML), we can uncover complex composition–structure–property relationships, accelerate materials discovery, and ultimately guide the rational design of advanced battery materials with enhanced performance and functionality.This project will accelerate the discovery and development of novel cathode, anode, and electrolyte materials, enabling the targeted design of high-performance and sustainable next-generation batteries. The goal is to improve capacity and stability as well as ionic and electronic conductivity of NIBs. Ab initio-based approaches (such as DFT and AIMD), thermodynamics and kinetics as well as machine learning and finite element methods will enable rapid and reliable screening of diverse chemical compositions and structural features across multiple length scales, from electronic and atomistic structures to micro- and mesostructures, accelerating the identification of optimal materials for next-generation batteries.Your profileYou have a master’s degree in computational physics, materials/mechanical engineering, chemical engineering, or a related field, with experience in computational modeling (ideally covering ab initio/DFT, multiscale modeling, and finite-element methods) and ML.You are proficient in English.You have strong scientific programming skills, particularly in Python. Experience with C++ or Fortran is an advantage.Knowledge of COMSOL, GeoDict, or other mesoscale/finite-element modeling software is a plus.You actively engage in scientific discussions, communicate your ideas clearly, and are motivated to further develop your academic profile through research and scientific collaboration. Our offerA four-year fulltime PhD positionA professional and personal development programme within Twente Graduate School.Gross monthly salary of € 3.204,- in the first year that increases to € 4.051,- in the fourth year.A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%.an inspiring, multidisciplinary and international environment with an attractive campus and lots of facilities for sports and leisure.The university provides a dynamic ecosystem with enthusiastic colleagues.excellent facilities for professional and personal development Information and applicationAre you interested in this position? Please send your application by email before September 20, 2026. Your application should include:A Curriculum Vitae, including a list of relevant courses and grades, as well as a list of publications and references, if applicable.A cover letter of no more than two pages, explaining your motivation, relevant qualifications, and specific interest in the position.Contact details for two referees whom we may contact during the selection process.Please send your application to Prof. Payam Kaghazchi p.kaghazchi@utwente.nl, with Sourav Baiju s.baiju@utwente.nl in CC, using the subject line “NANEXBAT”. Use of this subject line is mandatory.For further information about the position, please contact s.baiju@utwente.nl or p.kaghazchi@utwente.nl.Screening is part of the selection process. Applications will be reviewed on a rolling basis, and suitable candidates may be invited for an interview before the application deadline. We therefore encourage you to apply as early as possible.