PhD Position in Complex Digital Twins for Infrastructure

12 Mar 2024
Job Information

Organisation/Company

Delft University of Technology (TU Delft)

Research Field

Technology

Researcher Profile

First Stage Researcher (R1)

Country

Netherlands

Application Deadline

29 Apr 2024 - 21:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Digital Twins are proving to be a key innovation, bringing together insights from real-time data, simulation, and predictive analytics to mirror the complexities of physical assets. The relevance of these digital counterparts is in their capacity to provide a detailed, dynamic view of infrastructure systems, enabling proactive decision-making and enhancing operational efficiencies, resilience and sustainability. However, the construction and application of digital twins for infrastructure are inherently complex, necessitating a systematic approach that accounts for the many interactions within these systems and their susceptibility to environmental and operational variances.

Integrating complexity science, network science, and knowledge graphs becomes crucial in addressing these challenges in a complex infrastructure digital twin. Complexity science offers insights into systems' non-linear, emergent behaviours. In contrast, network science provides tools to model and interpret the interdependencies and the flow of information within the infrastructure networks. Knowledge graphs contribute by structuring vast, heterogeneous data into coherent, interconnected models, facilitating a deeper understanding and visualising the relationships and dependencies among various components. Together, these disciplines can create sophisticated, accurate digital twins that reflect the current state of infrastructure assets and predict their future conditions and responses to interventions. Thus, combining complexity science, network science, and knowledge graphs helps create complex digital twins of infrastructure, enabling better understanding, prediction, and management.

This PhD project seeks a candidate to develop an innovative approach for creating an integrated digital twin of infrastructure. This novel method will leverage diverse fields – complexity science, knowledge graphs, network science, game engines, graph neural networks, and multi-simulation integration – to capture the multifaceted nature of infrastructure systems. The research aims to construct a digital twin that incorporates:

• Knowledge graph: Semantic web and ontology engineering principles will be employed to establish a robust knowledge graph, organising and enriching large datasets.
• Dynamic behaviours: By utilising frameworks like agent-based modelling, the candidate will capture the intricate interactions within these systems
• Multi-simulation integration: The candidate will explore the integration of various simulation tools, enabling the digital twin to incorporate diverse aspects like traffic flow, energy consumption, and structural integrity, fostering a more comprehensive representation of real-world infrastructure behaviour.
• Predictive capabilities: Using machine learning methods such as graph neural networks to understand and infer explicit and latent relationships within the infrastructure networks, facilitating the study of emergent properties and future state prediction.

Furthermore, the candidate will utilise game engines to create immersive and interactive simulations, enhancing the digital twin's visualisation and user engagement. This research has the potential to redefine how we understand, manage, and predict the behaviour of complex infrastructure systems.

Requirements
Specific Requirements

We are seeking a dynamic and innovative PhD candidate passionate about shaping the future of infrastructure through digital twin technology. This position is ideal for individuals eager to work within a multidisciplinary environment, pushing the boundaries of how we design, monitor, and optimise civil infrastructure.

• Educational Background: Candidates must possess a Master's degree in one of the following domains: Civil Engineering, Computer Science, Data Science, or related fields.
• Experience: Demonstrated expertise in one or more areas such as digital twin development for infrastructure, complexity science applications in urban systems, network science, knowledge graphs, and graph neural networks. Experience in multi-simulation integration and agent-based modelling is highly valued.
• Technical Skills: Candidate should already possess or are willing to acquire proficiency in synthesising dynamic behaviors, data-driven relationships, and predictive capabilities within digital twins for infrastructure through advanced modeling frameworks like agent-based modeling, semantic web, ontology engineering, and machine learning methods like graph neural networks. Adept at integrating various simulation tools for a holistic digital twin representation, employing game engines for enhanced visualisation, with strong programming skills in Python or similar languages for IoT and sensor data integration.
• Interdisciplinary Collaboration: The ability to work effectively in a team spanning multiple disciplines. Candidates should possess excellent communication skills and be committed to integrating technological solutions with socio-economic and human behaviour considerations.
• Fieldwork: Willingness to engage in data collection, including potential travel to specific project sites for data collection and stakeholder engagement. International travel may be required.
• Start Date: The position is expected to commence in September 2024, with some flexibility for negotiation.

This PhD project presents a unique opportunity to engage in pioneering research on integrated digital twins for infrastructure. The project provides the flexibility to tailor the specific research topic within the broad technical framework outlined above, allowing candidates to leverage their existing skills and interests. We encourage applications from individuals passionate about this domain, regardless of meeting all the criteria listed. We welcome further inquiries to clarify project details through the provided contact information

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements .

Additional Information
Benefits

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

Selection process

Are you interested in this vacancy? Please apply no later than 29 April 2024 via the application button and upload the following documents, compiled into a single PDF file:

• Motivation letter (Max 1.5 pages).
• Academic CV.
• Research proposal on your plan for this research project (figures appreciated), including a brief literature review, research gap, anticipated research methodology and the expected outcome (max 2000 words or 2.5 pages: whichever is less).
• Your MSc thesis or a paper you wrote. If neither are available in English, include a 1-page abstract of your MSc thesis written in English.
• Transcripts from your BSc and MSc studies.

Please note:

• You can apply online. We will not process applications sent by email and/or post.
• A pre-Employment screening can be part of the selection procedure.
• Please do not contact us for unsolicited services.

Additional comments

The candidate will be based at the faculty of Civil Engineering and Geosciences (CEG) of the TU Delft at the Integral Design and Management (IDM) Section and associated with the DigiConstruct Lab. The department houses an interdisciplinary group of scientists, engineers and managers with expertise in Digital Twins, complex systems modelling and resilience engineering. While the candidate will be based at CEG, intense and continuous supervision will be provided by the Transportation and Logistics section of the Faculty of Technology, Policy and Management. The candidate can(are encouraged) engage with the Resilient Hydrotwin Project and further interact with the research partners in TU Einhoven and IITM Madras.

For more information about this vacancy, please contact Dr Ranjith Soman ([email protected] ).

Website for additional job details

https://www.academictransfer.com/338861/

Work Location(s)

Number of offers available

1

Company/Institute

Delft University of Technology

Country

Netherlands

City

Delft

Postal Code

2628 CD

Street

Mekelweg 2

Geofield

Where to apply

Website

https://www.academictransfer.com/en/338861/phd-position-in-complex-digital-twin…

Contact

City

Delft

Website

http://www.tudelft.nl/

Street

Mekelweg 2

Postal Code

2628 CD

STATUS: EXPIRED