3 PhDs on Digital Twins for autonomous systems in industrial manufacturing

Title:

3 open PhD positions on industrial Digital Twins for increased autonomy and decision support in manufacturing and production

Research area and project description:

We are looking for 3 PhDs to work on Digital Twins for autonomous systems in industrial manufacturing settings. In industrial settings, autonomous systems need to adapt to changing requirements and conditions while ensuring highest levels of quality in the production process and safe interaction with human co-workers and other manufacturing machines. To achieve this, we first need to determine in which processes we can introduce autonomous decision-making without affecting quality or safety requirements.

Afterwards, we need to bring digital twin capabilities to all respective levels of the manufacturing system, combining machine learning, decision-making, and optimisation with Digital Twins. This should allow systems to dynamically identify faults or even qualitative decline in the manufacturing process and adapt autonomously to compensate appropriately. In case an autonomous adaptation is not sufficient, operators should be notified with a suggestion of potential mitigation strategies.

In order for such techniques and process to be applied in an autonomous fashion, we require a unified and streamlined process. Introducing MLOps capabilities in operational manufacturing can be a viable starting point to achieve such a goal.

Finally, data and information about the production and manufacturing process needs to be provided to operators and stakeholders in real-time. Such a system would provide information based on the state of the system as well as the needs and competences of the user.

PhDs will join in the MADE REACT project, working with companies such as Lego, Novo Nordisk, Grundfos, Velux and/or CIM.

• Project description. For technical reasons, you must upload a project description. Please simply copy the project description above and upload it as a PDF in the application.

Qualifications and specific competences:

Applicants to the PhD position must have a relevant Master’s degree in the area of Computer Engineering, Computer Science, Robotics, or similar. A background in digital twin engineering, autonomous systems, and machine learning is required. An understanding of machine learning and MLOps is desired. Fault detection, multi-objective optimization, and online decision-making approaches are relevant areas of expertise for this position.

Previous experience with developing models, tools, or processes is desired.

The candidate has experience in the following fields.

• Cyber-physical modelling and simulation
• Digital Twins
• Autonomous Agents and Multi-Agent Systems
• Machine Learning and MLOps
• Probability & Statistics incl. Python/R

Place of employment and place of work:

The place of employment is Aarhus University, and the place of work is Department of Electrical and Computer Engineering, Helsingforsgade 10, 8200 Aarhus, Denmark.

Contacts:

Applicants seeking further information regarding the PhD position are invited to contact:

• Professor Peter G. Larsen, pgl@ece.au.dk (main supervisor)
• Associate Professor Lukas Esterle, lukas.esterle@ece.au.dk (co-supervisor)

For information about application requirements and mandatory attachments, please see our application guide. If answers cannot be found there, please contact:

• admission.gradschool.tech@au.dk

How to apply:

Please follow this link to submit your application.

Application deadline is 30 June 2025 at 23:59 CEST.

Preferred starting date is 1 August 2025.

Please note:

• Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.
• The programme committee may request further information or invite the applicant to attend an interview.
• Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background.

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