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

Scania; Stjärna; On-site; SEK 89,000 - 134,000; Full time

Job summary

Scania invites Master’s students to contribute to a focused project on LLM-assisted high-fidelity simulation within battery manufacturing. You will work with factory simulation models and explore AI support to streamline the simulation workflow.

The goal is to evaluate benefits, limitations and trust levels of AI-assisted simulation, deliver a demonstrator, and present findings to relevant organisation units. Start is January 2027 with a 20-week engagement (800 hours per student).

Qualifications

• Experience with simulation modelling and validation.
• Ability to analyse complex data and derive insights.
• Familiarity with AI/LLM concepts is a plus.

Responsibilities

• 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/modification of models, input data changes, rerunning simulations, and analysis/reporting of results.
• Compare LLM/AI-assisted work with conventional simulation approaches.
• Evaluate benefits, limitations, risks and the level of trust in results.
• Identify where human validation is required and where AI should not be used.
• Develop and demonstrate the final solution and document conclusions in a final report to Scania.

Skills

Simulation

Data science basics

AI/LLM concepts

Education

Master's degree program in simulation, data science or engineering

Job description

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.

• 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)

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