Doctoral student in Data-driven AI based Battery Aging Modeling Full-time

Doctoral Student In Data-driven Ai Based Battery Aging Modeling

Third-cycle subject: Energy and Environmental Systems, Energy Technology

To support the transition toward smarter and more sustainable building energy systems, the ALTBESS project, funded by the Swedish Energy Agency, focuses on advancing battery energy storage system (BESS) operation in buildings integrated with photovoltaic (PV) systems. The project investigates how data-driven methods and advanced optimization can improve battery performance while explicitly accounting for aging effects. The project is based on real-world case studies and combines simulation, optimization, and operational data.

The doctoral student will develop battery aging models tailored to the project's case studies using transfer learning and data-driven techniques, integrate the aging models into existing optimization and scheduling frameworks to enable aging-aware battery operation, and consolidate the developed models into a user-friendly software package suitable for researchers and practitioners. This position offers an opportunity to work at the intersection of AI, energy system optimization, and battery technologies in an applied research setting with academic and industrial collaboration.

Supervision: Dr Farzin Golzar is proposed to supervise the doctoral student. Decisions are made on admission.

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

• passed a second cycle degree (for example a master's degree), or
• completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
• acquired, in some other way within or outside the country, substantially equivalent knowledge

Completed coursework or equivalent training in energy systems and optimization methods is required, in accordance with the admission criteria for third-cycle (doctoral) studies. Interest or experience in areas such as machine learning, data analysis, battery modeling, sector coupling, and optimization methods is considered an advantage.

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6.

In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, candidates will be assessed upon their ability to:

• independently pursue his or her work
• collaborate with others,
• have a professional approach and
• analyze and work with complex issues.

After the qualification requirements, great emphasis will be placed on personal skills.

Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time. In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.

As a doctoral student, you are entitled to a workplace with many employee benefits and monthly salary according to KTH's Doctoral student salary agreement. Read more about Doctoral studies (PhD) | KTH | Sweden.