PhD in distributed digital twins (1.0 FTE)

A digital twin (DT) is a virtual representation of a physical object, system, or process synchronised with the real-world entity it replicates by using Internet of Things (IoT) technologies, such as sensors and actuators. By applying digital technologies, like AI, data analytics and computer simulations, a DT application can be used to experiment, simulate, analyse, and optimise the behaviour, performance and maintenance of the real-world counterpart, including its interaction with other objects or systems.

Critical infrastructures, such as a wastewater treatment ecosystem, employ DTs to monitor and control their facilities. While larger countries seek to decentralise their system, resulting in multiple independent DTs (e.g. one per remote rural area), European countries usually opt for a centralised wastewater system, resulting in highly coupled DT models (e.g. each representing specific stakeholders in the system). In both cases, it is necessary to ensure reasonable communication and coordination between independent DT models, in order to create better holistic digital ecosystems.

The project DDTClean (Intelligent Wastewater Treatment: Distributed Digital Twin for Clean Wate) funded by the NWO Merian Fund proposes a solution to the coordination and management of distributed DTs. A machine learning enhanced digital twin technology is proposed to facilitate the coordination in and across operational silos of the stakeholders. The proposed scalable distributed DT framework will enable remote monitoring and control of distributed waste treatment plans, thanks to Artificial Intelligence capabilities.

As part of the DDTClean project, the role of the PhD student will be to

• perform novel research in the area of scalable and distributed digital twin
• define models, create algorithms, develop software frameworks for a coordination engine
• scientifically test and validate the developed engine with multiple use cases
• establish collaborations with other project partners and beyond, integrating your results into complex systems where possible

The objective of this position is the production of a number of research articles in peer-reviewed scientific journals and conference proceedings, which together will form the basis of a thesis leading to a PhD degree (Dr) at the University of Groningen.

Organisation
Founded in 1614, the University of Groningen enjoys an international reputation as a dynamic and innovative institution of higher education offering high-quality teaching and research. Flexible study programmes and academic career opportunities in a wide variety of disciplines encourage the 35,000 students and researchers alike to develop their own individual talents. As one of the best research universities in Europe, the University of Groningen has joined forces with other top universities and networks worldwide to become a truly global centre of knowledge.

Within the Faculty of Science and Engineering, a 4-years PhD position is available at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence with the topic of distributed digital twins. The candidate would become a member of the Distributed Systems Group of the Computing Science Department and will work under the supervision of Dr. Dilek Düstegör and Prof. Alexander Lazovik.

The successful candidate should have:

• a Master's degree (or equivalent) in Computer Science or a related field
• good programming skills and experience with distributed computing frameworks
• knowledge of machine learning and data analysis, big data and cloud computing, IoT technologies
• excellent problem-solving and analytical skills
• high motivation in pursuing academic research
• effective communication (both written and spoken in English) and collaboration abilities

We offer you, following the Collective Labour Agreement for Dutch Universities:

• a salary of € 2,770 gross per month in the first year, up to a maximum of € 3,539 gross per month in the fourth and final year for a full-time working wee
• a holiday allowance of 8% gross annual income and an 8.3% year-end bonus
• a full-time position (1.0 FTE). The successful candidate will first be offered a temporary position of one year with the option of renewal for another three years
• prolongation of the contract is contingent on sufficient progress in the first year to indicate that a successful completion of the PhD thesis within the next three years is to be expected
• a PhD training programme is part of the agreement and the successful candidate will be enrolled in the Graduate School of Science and Engineering