PhD Position in Machine Learning for Digital Twins and Autonomous Cyber-Physical Systems
Title
PhD Position in Machine Learning for Digital Twins and Autonomous Cyber-Physical Systems
Research area and project description
We invite applications for a PhD position at Aarhus University within the Digital Twin Center for Open Research and Engineering (DT-CORE). The position is part of a large, ambitious research initiative building on the success of the DiT4CPS project and aims to advance the next generation of Digital Twin (DT) technologies for cyber-physical systems (CPS).
The successful candidate will contribute to cutting-edge research at the intersection of machine learning, simulation, and autonomous systems, with applications in robotics, manufacturing, and infrastructure systems.
The DT-CORE project focuses on addressing key challenges such as uncertainty, scalability, autonomy, and trustworthiness in Digital Twins.
The PhD candidate will work on machine learning-driven methods for robust, secure, and autonomous Digital Twins, with particular emphasis on the following tasks:
WP1 – Foundations
- Mutual Calibration
Develop ML-based methods to ensure consistency and synchronization across multiple interacting models within a Digital Twin. - Protection Against Security Attacks
Investigate learning-based approaches for detecting, mitigating, and preventing cyber-security threats in DT-enabled systems.
WP2 – Platform
- Automatic Digital Twin Generation
Design algorithms for automated configuration and deployment of DT components from models and metadata. - Test Scenario Generation
Develop intelligent scenario generation techniques (e.g., novelty detection, reinforcement learning) to improve system validation and testing.
WP3 – Autonomy
- Dependability to Remove Humans
Enable trustworthy autonomous adaptation through verification-aware learning and runtime monitoring. - Awareness of Reality Gap
Develop methods for detecting and mitigating discrepancies between physical systems and their digital counterparts. - Adaptive Fidelity of Models
Investigate adaptive, multi-fidelity modelling approaches driven by learning techniques to balance accuracy and computational cost.
Qualifications and specific competences
Applicants must hold a master’s degree or have completed at least one year of a master’s degree in computer engineering, computer science, or related field.
We are looking for a highly motivated candidate with:
1. A strong background in machine learning and deep learning
2. Experience with one or more of:
- Digital twin engineering
- Probabilistic modelling, Bayesian methods
- Reinforcement learning or control
- Simulation of Ordinary Differential Equations
- Anomaly detection or security in CPS
4. Interest in interdisciplinary research combining ML with engineering systems
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 N., Denmark
Contacts:
Applicants seeking further information regarding the PhD position are invited to contact:
- Professor Peter Gorm Larsen, e-mail: Pgl@ece.au.dk (main supervisor)
- Associate Professor Lukas Esterle, e-mail: le@ece.au.dk (co-supervisor)
- Associate Professor Cláudio Ângelo Gonçalves Gomes, e-mail: claudio.gomes@ece.au.dk (co-supervisor)
How to apply
Please follow this link to submit your application.
Application deadline is 01 June 2026 23:59 CEST.
Preferred starting date is 01 September 2026.
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.
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. Salary and terms of employment are in accordance with applicable collective agreement.
Please note in your application that you found the job at Jobindex
