PhD scholarship in Additive to Predictive Manufacturing for Multistorey Construction using Learning by Printing and Networked Robots (AM2PM) - DTU Construct

The Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU) invites an excellent candidate to join our team for this full-time position.

Responsibilities 
The role in AM2PM, an EU funded research project, involves conducting innovative theoretical and experimental research in Building Information Modeling (BIM), Digital Twin Construction (DTC), robotics and computing, construction production processes, and life cycle and sustainability analysis (LCA). The successful candidate will be responsible for conducting cutting-edge research in the field of expertise, collaborating with faculty and other researchers as part of a strong interdisciplinary research team, and will be involved in collaborations with industrial partners and international academic collaborators. It is expected that the candidate can publish research findings and contribute to the academic community through seminars and conferences. Opportunities for participation in teaching and mentoring are required and will be given.

You will have an exciting opportunity to work on the future paradigm for the construction of multistorey buildings in which a comprehensive system-of-systems approach intertwines (a) networked multi-agent human-robotic systems that work collaboratively in a well-coordinated and safe manner, (b) computational design and digital manufacturing of components, (c) design of sustainable materials, (d) Artificial Intelligence (AI) models to predict and control the manufacturing process and (e) a Digital Twin (DT) incl.  Building Information Modeling (BIM) information backbone that enables cohesive operation of the design and production system. 

This position is part of the EIC Pathfinder Project AM2PM: “Additive to Predictive Manufacturing for Multistorey Construction using Learning by Printing and  etworked Robots” (AM2PM).

The goal of AM2PM is to develop and experimentally validate a complex digital infrastructure that integrates sustainable material design, computational structural design, 3D printing robots, sensors, safe and efficient human machine interfaces, processing units, and software components into a cyber physical construction system. The result would be transformational, achieving a 50% reduction in material use, reducing embodied carbon by up to 29 million tons and potentially savings of more than €11 billion annually in the construction of buildings.

Qualifications
You should have completed a two-year master's degree (120 ECTS points) in Civil, Architectural, Environmental, Electrical, Mechanical or Industrial Engineering, Autonomous Systems, Computer Science, or related discipline, or a similar degree with an equivalent academic level. You will be expected to have:

• Excellent written and oral communication skills
• Research and data analysis
• Excellent analytical and critical thinking skills
• Ability to work independently and as part of a team
• Knowledge of relevant research methodologies and tools

If you want to be successful you are expected to be self-motivated and to publish your results in international peer-reviewed journals.

Having experience in publishing scientific results as well as working with industry is considered an asset in these positions. Applicants must also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab. 

A desire to engage in cross-disciplinary research at the intersections of civil, mechanical, electrical engineering and computer science is important. It will be relevant to have deep experience of at least one of the following areas:

• Construction production engineering
• Construction informatics, and in part related to Building Information Modelling (BIM), ontologies, process modeling, databases, open data standards such as Industry Foundation Classes (IFC), and linked data
• Sensors as part of Internet of Things (IoT) and integration of sensory information in simulation models as part of Digital Building Twins (DBT) during run-time
• Life cycle and sustainability analysis (LCA)
• Data processing, incl. artificial intelligence and machine learning
• Serious gaming incl. AR/AV/VR
• Automation and robotics, incl. human-machine interaction

Approval and Enrolment 
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education. 

Assessment<