The Pervasive Systems research group at the University of Twente is looking for a PhD and two EngD candidates to perform research and development on a multidisciplinary project involving Digital Twins, Augmented Reality, and Deep Learning. The main research objective for the PhD position is to research efficient Deep Learning techniques to combine computer vision with multiple sensor data streams for infrastructure health monitoring. Augmented Reality will be integrated in the Structural Health Monitoring (SHM) for more efficient maintenance and inspection. Lightweight but accurate vision-based inspection methods and algorithms must be developed to be run efficiently on resource-constrained devices such as drones and smartphones. A main challenge is how to fuse large streams of the data flows from the in-situ sensors and remote vision sensors into the virtual reality.

The prospective candidates are expected to perform high quality and internationally visible research that gets published at top tier conferences and journals. Candidates will work at the Pervasive Systems Research group, Department of Computer Science, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente in the Netherlands. The candidates are expected to collaborate with project partners including TUDelft, TU/e, and Leiden University, but also the asset owners, engineering companies, and research institute TNO.

The project
The vacancies are within the scope of the SUstainaBle and reLIable Macro stEel infra-structures (SUBLIME) project, and is a collaboration between 4 universities, industry, and asset owners. The project is funded by the national research foundation NWO, together with the supporting partners from industry. It merely deals with the degradation of large steel infrastructures through corrosion and fatigue which results in significant unforeseen maintenance or replacement costs. The goal of this project is to improve our knowledge on how to increase the sustainability and reliability of such infrastructures, combining a technical and governance perspective. Predictive digital twins will be developed that integrate structural health monitoring and prediction models.

• You have a Master's degree in either Computer Science, Electronic Engineering, Telecommunication Engineering, or Mathematics.
• You have experience in relevant areas such as signal processing, computer vision, embedded systems, data analysis, machine learning and deep learning.
• Candidates should be interested in solving analytical tasks and develop prototypes, combined with intermediate programming skills (e.g. in R, Python or C-something)
• Prior experience in generative models, transfer learning, federated learning, deep learning for embedded devices is a plus (with frameworks such as TensorFlow and Keras).
• Moreover, we are looking for a strong personality to defend your research ideas not only at university but also in an industrial context.
• We encourage applications from outstanding candidates with a keen interest in doing research in novel methods for real-world applications.
• You have good communication skills and you have a strong interest in operating at the crossroads of different disciplines
• You have an excellent command of English (C1; above IELTS 7 or equivalent)
• You are able to do independent research and have publication skills.

• As a PhD student at UT, you will be appointed to a full-time position for four years, with a qualifier in the first year, within a very stimulating and exciting scientific environment;
• The University offers a dynamic ecosystem with enthusiastic colleagues;
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAO-NU);
• You will receive a gross monthly salary ranging from € 2.541,- (first year) to € 3.247,- (fourth year);
• There are excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• You will have a training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• We encourage a high degree of responsibility and independence, while collaborating with close colleagues, researchers and other staff.