PhD: System Orchestration for Predictive Manufacturing and Robotic Construction
3 weken geleden
Job description
Are you passionate about Artificial Intelligence (AI) and construction robotics and committed to improving the performance of the built environment? If you are motivated to discover how Digital Twins (DTs), AI and multi-agent human-robotic systems can work collaboratively on site to transform the construction industry and achieve 50% reduction in material use, then this position is for you.
Context
Currently, the EU is not on track to achieve climate neutrality by 2050 or reduce net greenhouse gas emissions by at least 55% by 2030, as targeted by the EU Green Deal and EU Fit for 55, respectively. The sector accounts for almost 40% of CO2 emissions and overall energy consumption in industrialized and developing countries. Furthermore, it is also responsible for 50% of raw material consumption and 35% of all waste. In this context, concrete is the most used building material globally due to its low production costs, strength, and availability of raw materials, but its use of cement makes it a significant contributor to climate change. An estimated 8% of global CO2 emissions are caused by cement production. Therefore, strategies are urgently needed to reduce cement consumption, limit excessive material use and minimize waste in line with the European Green Deal and the New European Bauhaus. In this regard, 3D concrete printing, DTs, AI and construction robotics hold significant potential.
Relying on a system-of-systems approach, the AM2PM project aims to design, test, and implement an AI-enabled DT infrastructure for predictive manufacturing and robotic construction capable of simultaneously optimizing the building design geometry, material definition, and manufacturing process at all stages of a construction lifespan, thereby transforming the construction site into a robotized Cyber Physical System (CPS).
Research Activities
TU/e will contribute with research and configuration of the CPS orchestration mechanism, which operationalizes the interaction between multiple physical and digital 3D printing systems and construction robots. This implies creating a dynamic real-time connection through which the DT can affect the 3D printing and physical processes on site, and the physical processes can inform the DT and the performed computations, leading to a real-time feedback loop and control of the 3D printing process and the robotic physical site. The resulting CPS understands the predictive manufacturing and construction site processes and uses them to coordinate the robotic activities. Further investigations include enabling data processing at the edge and allowing construction robots and 3D printing devices to process data (semi-) autonomously, relying on communication with the DT infrastructure as a data warehouse, simulation, and actuation platform.
Based on the above, the main research activities include:
Define a system architecture that orchestrates the interaction of multiple 3D printers and on-site robots. Create the multimodal DT infrastructure with a dynamic real-time connection through which the DT can schedule and affect the physical additive manufacturing processes, and the physical additive manufacturing process can inform the DT and the performed computations. Develop and configure the digital representations of the physical assets in cyberspace relying on BIM, Semantic Web technologies, robotic models and cloud technologies. Establish the communication between the digital and the physical counterparts relying on open industrial connectivity standards, integration software and sensing technology. Investigate and define an appropriate edge computing architecture that prioritises the relevant aspects of data placement and analysis, data security, learning and robot orchestration.You will become part of the Information Systems in the Built Environment (ISBE) group at the Department of the Built Environment. You will join a recognized team under the supervision of Dr. Ekaterina Petrova and Dr. Pieter Pauwels from the ISBE group and Dr. Rob Wolfs from the 3D Concrete Printing (3DCP) group. Not only will you be part of a challenging and innovative project, but you will also be able to learn, apply and improve diverse data handling and software development techniques in support of DTs, CPS, AI-driven additive manufacturing and robotic construction.
Furthermore, you will collaborate closely with researchers from the Technical University of Munich, Technion, Technical University of Denmark and WASP.
Job requirements
You hold a master’s degree (or an equivalent university degree) in Architectural Engineering, Civil Engineering, Computer Science, Software Engineering, Robotics, or similar. You have a strong background in Building Information Modelling, Digital Twins and/or robotic construction. You have experience or interest in working with data handling, semantic data modelling, knowledge representation and linked data technologies. You have experience or interest in working with Artificial Intelligence and edge computing. You have knowledge and experience with programming and software development. You have a research-oriented attitude and good communication skills. You have an ability to work in an interdisciplinary team and interested in collaborating with industrial partners. You are motivated to develop your teaching skills and supervise students. You are fluent in spoken and written English (C1 level).
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks. Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670). A year-end bonus of 8.3% and annual vacation pay of 8%. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. An excellent technical infrastructure, on-campus children's day care and sports facilities. An allowance for commuting, working from home and internet costs. A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.-
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