KTH Royal Institute of Technology is offering a fully funded PhD position in Data-driven AI-based Battery Aging Modeling within the School of Industrial Engineering and Management. The research is part of the ALTBESS project, funded by the Swedish Energy Agency, and focuses on advancing battery energy storage system (BESS) operation in buildings integrated with photovoltaic (PV) systems. The project aims to develop advanced machine learning models to understand, predict, and optimize battery degradation, contributing to more sustainable and reliable energy infrastructures. The doctoral student will work on real-world case studies, combining simulation, optimization, and operational data to create battery aging models using transfer learning and data-driven techniques. These models will be integrated into existing optimization and scheduling frameworks to enable aging-aware battery operation, and the results will be consolidated into a user-friendly software package for researchers and practitioners. The position offers a unique opportunity to work at the intersection of AI, energy system optimization, and battery technologies, with both academic and industrial collaboration. Supervision will be provided by Dr Farzin Golzar, Assistant Professor at KTH. The ideal candidate will have a strong background in energy systems, optimization methods, and a keen interest or experience in machine learning, data analysis, battery modeling, and sector coupling. Proficiency in English (equivalent to English B/6) is required. Applicants must hold a second cycle degree (e.g., master's) or have completed at least 240 higher education credits, with at least 60 at the second-cycle level, or possess equivalent knowledge. Personal skills such as goal orientation, independence, collaboration, and analytical ability are highly valued. The position is full-time, temporary, and offers a monthly salary according to KTH's doctoral student salary agreement, along with employee benefits. The employment duration is up to four years, corresponding to full-time doctoral education. The application deadline is March 12, 2026. To apply, candidates must submit a complete application through KTH's recruitment system, including a CV, application letter, diplomas, grades, language certificates, and relevant publications. All documents must be certified and translated if necessary. For more information, visit the official job posting or contact Dr Farzin Golzar at [email protected]. Keywords: battery aging modeling, AI, machine learning, energy storage systems, energy technology, optimization, data-driven methods, battery degradation, energy transition, photovoltaic systems.

KTH Royal Institute of Technology is offering a fully funded PhD position in Data-driven AI-based Battery Aging Modeling within the School of Industrial Engineering and Management. The research is part of the ALTBESS project, funded by the Swedish Energy Agency, and focuses on advancing battery energy storage system (BESS) operation in buildings integrated with photovoltaic (PV) systems. The project aims to develop advanced machine learning models to understand, predict, and optimize battery degradation, contributing to more sustainable and reliable energy infrastructures. The doctoral student will work on real-world case studies, combining simulation, optimization, and operational data to develop battery aging models using transfer learning and data-driven techniques. These models will be integrated into existing optimization and scheduling frameworks to enable aging-aware battery operation, and consolidated into a user-friendly software package for researchers and practitioners. The research sits at the intersection of AI, energy system optimization, and battery technologies, offering opportunities for academic and industrial collaboration. Supervision will be provided by Dr Farzin Golzar, Assistant Professor at KTH. Applicants should have a strong background in energy systems and optimization methods, with additional experience or interest in machine learning, data analysis, battery modeling, and sector coupling considered advantageous. Proficiency in English (equivalent to English B/6) is required. The position is open to candidates with a second cycle degree (e.g., master's) or equivalent qualifications, and selection will emphasize personal skills such as goal orientation, independence, collaboration, and analytical ability. The position is fully funded, with a monthly salary according to KTH's doctoral student salary agreement, and includes full-time employment for up to four years. The successful candidate will benefit from KTH's creative and dynamic research environment, employee benefits, and opportunities for professional growth. The application deadline is March 12, 2026. For more information and to apply, visit the official KTH job posting. Keywords: battery aging modeling, AI, machine learning, energy storage systems, energy technology, optimization, data-driven methods, battery degradation, energy transition, photovoltaic systems.