30 hp - LLM-Assisted High-Fidelity Simulation in Battery Manufacturing

30 hp - LLM-Assisted High-Fidelity Simulation in Battery Manufacturing

Introduction

Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.

Background

Within Scania's battery manufacturing, simulation is used to analyse and test manufacturing and logistics solutions in a virtual environment before implementing changes in production. This makes it possible to evaluate different solutions without interfering with ongoing production.

Simulation typically requires specialised knowledge and competence to create, validate, verify, update and interpret simulation models and their results. This limits how broadly simulation can be applied in the organisation.

The thesis work will investigate how Large Language Models (LLMs) and Artificial Intelligence (AI) can support simulation work and make it more efficient and accessible.

Objective

The objective is to evaluate how LLM/AI can support high-fidelity simulation in battery manufacturing and to develop a demonstrator showing where the technology can provide value, its limitations and what level of trust can be assigned to the results.

Job description

• The thesis worker(s) will work with factory simulation models and investigate how LLM/AI can support selected parts of the simulation process. The work will include:
• Understand the current simulation process and select relevant use cases.
• Develop and evaluate high-fidelity factory simulation models.
• Apply LLM/AI support to selected parts of the simulation process, including creation and modification of models, changes to input data, rerunning simulations, and analysis and reporting of results.
• Compare LLM/AI-assisted work with conventional simulation work.
• Evaluate benefits, limitations, risks and the level of trust that can be assigned to the results.
• Identify where human validation is required and where LLM/AI should not be used.
• Develop and demonstrate the final solution.
• Document conclusions and recommendations in a final report to Scania and present the results to relevant parts of the organisation.

Education/program/focus

• Relevant Master's programme with a focus on simulation, data science or engineering.
• Number of students: 1-2
• Start date for the thesis work: January 2027
• Estimated time required: 20 weeks (800 hours per student)

Contact persons and supervisors

Anders Siljeström, Logistics Engineering Manager, anders.siljestrom@scania.com, +46855371808

Jesper Gans, Logistics Developer, jesper.x.gans@scania.com, +46765356769

Application

Your application must include a CV, personal letter and transcript of grades.

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.

Publication date until:2026-11-22

Requisition ID: 34747

Number of Openings: 1.0

Part-time / Full-time: Full-time

Permanent / Temporary: Temporary

Country / Region: SE

Location(s):
Södertälje, SE, 151 38

Required Travel: 0%

Workplace: On-site