At a glance

Location: Göteborg, Sweden
Work type: Onsite

What they are looking for

PythonMatlabMachine learningTime-series machine learningGt-suite

Location: Göteborg, SE, 417 15

Master Thesis: Development & evaluation of ML models for catalysts in exhaust aftertreatment systems

Location

Göteborg, SE, 417 15

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Position Type

Student

Permanent/Temporary

Temporary

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

Background

The transportation industry is undergoing one of the largest transformations in its history. Digitalization, sustainability, and artificial intelligence are changing how vehicles are designed, tested, and developed. At Volvo Group, we want to remain at the forefront of this transformation by developing innovative and sustainable technologies that keep society moving.

Combustion engines will continue to play a role in the future transportation portfolio. To meet demanding emissions requirements, they rely on advanced exhaust aftertreatment systems that reduce pollutants under highly dynamic operating conditions and throughout the vehicle’s lifetime.

Developing these systems is complex, expensive, and time-consuming. Detailed physical models can provide valuable insight into catalyst behavior, but they may also require considerable resources. Machine learning and other AI methods may offer opportunities to develop faster models, explore large datasets, and accelerate engineering workflows. However, they also have the limitation of not being a physical interpretation of the system, and controlling quality and data requirements are still a challenge.

In this thesis, you will explore the boundary between physical modelling and machine learning. You will develop and compare ML-based surrogate models for dynamic catalyst systems, investigate how model architecture and calibration affect their performance, and identify when these methods provide real engineering value.

Objective

The objective of this thesis is to systematically evaluate compact machine learning architectures for dynamic modelling of catalysts used in exhaust aftertreatment systems, such as DOC, SCR, ASC, and DPF systems.

The study will assess how model architecture, hyperparameters, calibration, and training-data coverage affect prediction accuracy, robustness, extrapolation, physical interpretability, data requirements, and computational performance. The results will support the development of common guidelines for the reproducibility, validation, quality assessment, and appropriate use of ML-based catalyst models.

The project will also explore how physical models, machine learning, and automated modelling pipelines can be combined to manage information across multiple scales, from molecular and catalyst behavior to complete vehicle operation.

Candidate Profile

You will receive active support and guidance from experienced simulation engineers. In return, we expect you to take initiative, work systematically, and drive the project forward within the agreed scope and timeframe.

You are a curious, analytical and motivated master’s student with an interest in combining engineering fundamentals, simulation, data analysis, and machine learning.

Are pursuing an MSc in Chemical Engineering, Applied Physics, or a related engineering or scientific field.

Have a solid understanding of, and interest in, reaction engineering and chemical kinetics.

Have experience with MATLAB and Python.

Are interested in developing your knowledge of statistics, thermodynamics, exploratory data analysis, and machine learning.

Enjoy solving complex technical problems and transforming data into engineering understanding.

Are comfortable working independently and taking responsibility for your part of the project.

Are proficient in English, both verbally and in writing.

Experience with any of the following will be considered an advantage:

Time-series machine learning

GT-SUITE

Combustion engines

Exhaust aftertreatment systems

Practical info

Supervisors: Andres Suarez, 1D simulation engineer & Magnus Walander, Senior 1D simulation engineer.

Last Application date: 15:th of November

We value your data privacy and therefore do not accept applications via mail.

Who we are and what we believe in

We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.

Trucks Technology & Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need.

Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.