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Siemens Industry Software NV

Just Landed

PhD Position in AI-driven computational models for battery degradation Siemens Industry Software NV in Belgium

Degree Level

PhD

Field of study

Mechanical Engineering

Funding

Available

Deadline

Jan 1, 2027

Country flag

Country

Belgium

University

Siemens Industry Software NV

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Keywords

Mechanical Engineering
Electrical Engineering
Aerospace Engineering
Mathematics
Predictive Modeling
Electrochemistry
Digital Twin Technology
Active Learning
Thermal Dynamics
Multiphysics Simulation
Pde
Physics
ML

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About this position

This PhD project focuses on AI-driven computational models for battery degradation within the EU-funded TRAIN-FAST Marie Skłodowska-Curie Doctoral Network. The research is hosted by Siemens Industry Software NV in Leuven, Belgium, in collaboration with Politecnico di Torino, where the PhD degree will be awarded.

The project sits at the intersection of battery science, simulation, and data-driven modelling. The doctoral researcher will contribute to the development of a digital twin for predictive ultra-fast charging, supporting the broader goal of advancing electric mobility and safer, faster battery charging technologies. The work is embedded in an interdisciplinary network that brings together engineering, data science, and industrial partners.

Research activities will center on building multi-scale, multi-physics simulation workflows that capture coupled electro-chemical, thermal, and mechanical degradation processes in batteries. The student will develop physics-based coupled models, investigate degradation phenomena for specific battery chemistries, and create fast surrogate workflows that connect detailed physical models to lighter models suitable for control-oriented use.

A major methodological theme is the combination of machine learning and model order reduction to approximate complex partial differential equations over high-dimensional parameter spaces. The role also involves validating models against experimental data and results from the scientific literature, as well as communicating findings through publications and conference presentations.

The student will join a multidisciplinary innovation team in Leuven working on energy topics including batteries and hydrogen, with application areas ranging from automotive to aerospace. The project also offers close collaboration with other doctoral candidates in the TRAIN-FAST network, industrial partners, and academic collaborators across the consortium.

The position is part of an international doctoral network with joint training activities, secondments, workshops, and conferences.

Funding details

Available

What's required

Applicants must have a two-year Master's degree (120 ECTS) or an equivalent academic level, and may apply before obtaining the degree but cannot start until it has been awarded. A background in modelling and simulation in multi-physics domains is expected, especially electrochemistry and mechanical domains, with experience in numerical modelling being advantageous. Strong skills in machine learning and model order reduction are desirable, along with familiarity with battery technologies, electrochemical-mechanical degradation phenomena, cell design, solving partial differential equations in high-dimensional parameter spaces, operator learning, active learning and Bayesian optimisation. Excellent written and oral English are essential. Candidates must not already hold a doctoral degree and must satisfy the MSCA mobility rule for Belgium: no more than 12 months of residence or main activity in Belgium in the 36 months before recruitment.

How to apply

Submit the application online via the Apply now link. Upload one English PDF containing your motivation letter, CV, grade transcripts, and BSc/MSc diploma with the official grading scale description. Applications received after the deadline will not be considered.

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