PhD Position: AI-driven computational models for battery degradation

Siemens AG; Vlaams-Brabant; On-site; EUR 55,000 - 65,000; Full time

Benefits offered by this job

Mobility allowance

Family allowance

Job summary

The PhD position at Siemens Industry Software NV, in collaboration with Politecnico di Torino, offers a unique opportunity to develop AI-driven, multi-physics models for battery degradation as part of the TRAIN-FAST network.

You will work in Leuven, Belgium, within an interdisciplinary team focusing on ultra-fast charging, ML-accelerated MOR methods, and close industry-academia collaboration; mobility and family allowances are provided under MSCA regulations.

Qualifications

• Two-year master's degree or equivalent.
• Experience with numerical modelling is advantageous.
• Excellent English communication skills.
• Doctoral candidates only; you may apply before obtaining your master's but cannot start before it.

Responsibilities

• Develop physics-based coupled electro-chemical and mechanical models for batteries.
• Investigate degradation phenomena for specific battery chemistries.
• Derive ML-accelerated ROM workflows for high-dimensional parameter spaces.
• Validate numerical models against experimental data and literature.
• Disseminate findings through publications and conferences.

Skills

Numerical modelling

English proficiency

Industrial research experience

Education

Two-year master's degree

Job description

PhD Position: AI-driven computational models for battery degradation

525391

12-Oct-2026

Research & Development

Siemens Industry Software NV

Profesional Junior

Temporal

PhD Position in AI-driven computational models for battery degradation

As part of the recently launched MSCA-DN (Marie Skłodowska-Curie Actions Doctoral Network) project funded by the European Union, titled TRAIN-FAST (Training Researchers in Adaptive, Integrated and Safe Technologies for Ultra-Fast Battery Charging), Siemens Industry Software NV (SISW) has a PhD position to collaborate in the development of a Digital Twin for predictive ultra-fast charging. This PhD is in collaboration with Politecnico di Torino (POLITO) where the PhD degree will be awarded.

Transportation accounts for approximately one-third of global energy-related carbon dioxide emissions and is undergoing a profound transition towards electric mobility and renewable energy integration. Ultra-fast charging technology is critical for providing charging convenience comparable to conventional refuelling and for accelerating the widespread adoption of electric vehicles. The aim of TRAIN-FAST is to educate the next generation of leading scientists and engineers who will advance ultra-fast charging technology within the broader electric-mobility ecosystem. The programme adopts an interdisciplinary approach, combining advanced engineering, data science, techno-economic analysis, and socio-behavioural research to address complex cross-sector challenges. As part of this project, you will have the opportunity to conduct pioneering research at the forefront of battery and charging technology. Please refer to the TRAIN-FAST project website ( https://train-fast.dtu.dk/ ) for further information about the project and the PhD opportunities available at the other recruiting organisations.

Responsibilities and qualifications

The PhD researcher will focus on the following activities:

• Develop a multi-scale and multi-physics simulation workflow to accelerate battery material screening and cell design capturing coupled electro-chemical, thermal, and mechanical degradation.
• Employ a combination of ML (Machine Learning) and MOR (Model Order Reduction) techniques to efficiently approximate coupled multi-physics partial differential equations across high-dimensional parameter spaces.

These activities will be carried out in close collaboration with the other PhD candidates within the TRAIN-FAST network and with strong support from the industrial partners.

You should have a passion for research in industrial context and a background in modeling and simulation in multi-physics domains (especially in electro-chemistry and mechanical). You will become part of a multidisciplinary innovation team located in Leuven (Belgium) with a focus on energy topics such as batteries, hydrogen, and covering several application fields from automotive to aerospace. We combine these innovation themes with numerical methods such as MOR and ML in close collaboration with academia.

Your specific tasks will cover:

• Develop physics-based coupled electro-chemical and mechanical models for batteries.
• Investigate and derive degradation-related phenomena for specific battery chemistries.
• Develop workflows linking physics-based models to light/fast models needed to support control strategies by operator-learning as ML-accelerated ROM with active learning and Bayesian optimisation guiding exploration of the high-dimensional parameter spaces
• Evaluate and validate numerical models and methods against experimental data and results reported in the scientific literature.
• Disseminate research findings through scientific publications and presentations at international conferences.

You are expected to take an active role in the international TRAIN-FAST network, participate in secondments at partner organisations, and engage in the network's joint training activities, workshops, and conferences.

Eligibility criteria

• Two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.
• Experience with numerical modelling or related topics will be advantageous.
• Strong communication skills in English, both written and oral, are essential.
• Researchers must be doctoral candidates, i.e. not already in possession of a doctoral degree. You may apply prior to obtaining your master's degree but cannot begin before having received it. Applicants of any nationality are welcome.
• Note that the MSCA-DN mobility clause applies: the applicant must not have resided or carried out their main activity (work, studies, etc.) in Belgium for more than 12 months in the 36 months immediately before the date of recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.

We are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.

Additional information

• Recruitment: October 2026 - February 2027
• Duration: 36 months
• The doctoral candidate will be employed by Siemens Industry Software NV for the full duration
• The doctoral candidate will receive an attractive salary in accordance with MSCA regulations, consisting of (monthly):
• a gross living allowance of €4010 (country correction coefficient applies) including salary, tax and insurance;
• a mobility allowance of €710;
• if applicable, a family allowance of €660.
• The exact (net) salary will be confirmed upon appointment.

Siemens Digital Industry Software NV evolved from LMS International, one of the first spin-offs of KU Leuven, and now operates as the Simulation and Test Solutions division (SIM) within Siemens Digital Industries Software (DI SW), a recognized leader in Product Lifecycle Management (PLM). Through the Simcenter portfolio and Xcelerator platform, SISW provides a comprehensive range of digital and test solutions for vital economic industries such as manufacturing, energy and transportation. Our technologies combine the physical and digital worlds in a continuous flow of data across the entire product lifecycle, from initial concept to deployment and operation.

Transform the everyday and shape the future of your career with us

Siemens Digital Industries Software is your place to thrive. Whether you’re a current student, recent graduate, or postgraduate, our talent hiring programs offer the trust and freedom to succeed while building a strong foundation for your professional career. You will have the chance to work on real projects, contribute to our future success, and make an impact for our customers.