Summary:
Aarhus University is inviting applications for a PhD fellowship within the Digital Twin Center for Open Research and Engineering (DT-CORE). This position aims to advance Digital Twin technologies for cyber-physical systems, focusing on machine learning, simulation, and autonomous systems. The successful candidate will tackle key challenges in the field through innovative research methods.
PhD Position in Machine Learning, Aarhus University, Denmark
Designation:
PhD Candidate
Research Area:
- Machine Learning
- Digital Twins
- Autonomous Systems
- Cyber-Physical Systems
Location:
Aarhus University, Department of Electrical and Computer Engineering, Helsingforsgade 10, 8200 Aarhus N., Denmark
Eligibility/Qualifications:
| Criteria | Requirements |
|---|---|
| Educational Background | Master’s degree or at least one year of master’s in computer engineering, computer science, or a related field |
| Technical Skills | – Background in machine learning and deep learning – Experience in: Digital twin engineeringProbabilistic modelling, Bayesian methodsReinforcement learning or controlSimulation of Ordinary Differential EquationsAnomaly detection or security in CPS- Solid programming skills (e.g., Python, C++, etc.) |
| Research Interest | Interest in interdisciplinary research combining ML with engineering systems |
Job Description:
The PhD candidate will work on the following tasks within the DT-CORE project:
- WP1 – Foundations:
- Mutual Calibration: Develop ML-based methods for model consistency and synchronization.
- Protection Against Security Attacks: Investigate ML approaches for detecting and mitigating cybersecurity threats.
- WP2 – Platform:
- Automatic Digital Twin Generation: Design algorithms for the automated configuration of DT components.
- Test Scenario Generation: Develop intelligent scenario generation techniques for system validation and testing.
- WP3 – Autonomy:
- Dependability to Remove Humans: Enable trustworthy autonomous adaptation.
- Awareness of Reality Gap: Develop methods to mitigate discrepancies between physical and digital systems.
- Adaptive Fidelity of Models: Investigate multi-fidelity modelling approaches driven by learning techniques.
How to Apply:
Interested candidates can submit their application via the Aarhus University application link. Applicants must upload a project description, which can be copied from the provided project description.
Last Date to Apply:
01 June 2026, 23:59 CEST
For more information, candidates can contact:
- Professor Peter Gorm Larsen: pgl@ece.au.dk
- Associate Professor Lukas Esterle: lukas.esterle@ece.au.dk
- Associate Professor Cláudio Ângelo Gonçalves Gomes: claudio.gomes@ece.au.dk
For more details about application requirements and mandatory attachments, please refer to the application guide.
