Description:

Traditional infrastructure inspection methods rely heavily on human site visits and vision-based tools, such as RGB cameras. While effective, these methods face limitations in capturing three-dimensional details and other critical information. To address this gap, this research will explore non-contact sensing devices and develop real-time multi-modal deep learning (DL) models capable of integrating data from multiple sources to improve detection accuracy and data quality. The project includes:

• Utilizing non-conventional imaging sensors like LiDAR, laser Doppler vibrometer (LDV), thermal, and hyperspectral cameras.

• Developing and testing Multi-modal DL models for data fusion and enhanced accuracy.

• Creating a prototype standalone device equipped with sensors and AR capabilities, such as Microsoft HoloLens 2, for real-time visualization of processed information.

• Demonstrating the technology’s performance on real-world infrastructure during collaborations with FACTOR SOC and SMARTEC.

UE-funded research - HE - Excellent Science - MSCA

Qualifications:

We welcome applications from engineering students who are eager to contribute to this transformative research. Candidates should have or a the following qualifications:

Experience with programming languages such as Python or MATLAB.

Understanding of Structural Health Monitoring (SHM) principles.
Knowledge of sensor technologies (e.g., LiDAR, thermal imaging, hyperspectral cameras).
Ability to conduct literature reviews and apply theoretical concepts to practical challenges.
Willingness to collaborate with interdisciplinary teams and industry partners.
Familiarity with deep learning and data fusion methodologies is a plus.

Please submit your application, including a cover letter, CV, and any relevant project work or publications, to the link below.

Position 2: Integrated drone systems for inspection and analysis of urban comunities and infrastructures.

Date of announcement: 13/08/2023.

Description:

Many strategic buildings and infrastructures in Europe have exceeded their design lifespan, raising concerns about progressive collapse and citizen safety. Current inspection methods, which rely on visual assessments by professional engineers, are time-consuming and costly. This research aims to revolutionize the data collection process by utilizing UAVs (drones) equipped with hybrid sensors to automate data gathering and analysis. Key objectives include:

• Designing and installing customized hybrid sensors (e.g., hyperspectral cameras, LiDAR) on a swarm of UAVs for efficient data collection.

• Developing ML algorithms to process collected data, identifying geometrical, structural, and thermal parameters of buildings.

• Creating a system prototype with three AI-driven UAVs that prevent collisions while gathering and processing data.

• Calibrating computational models for progressive collapse analysis using the collected data.

• Implementing the findings in a GIS model to facilitate decision-making for stakeholders.

UE-funded research - HE - Excellent Science - MSCA

Qualifications:

This position is ideal for engineering students passionate about applying advanced technology to real-world challenges. Candidates should possess the following qualifications:

Knowledge of structural engineering principles and risk assessment methods.
Familiarity with UAV technology and sensor integration is a plus.
Understanding of machine learning concepts and their applications.
Experience with programming tools such as Python, MATLAB, or similar for data analysis and algorithm development.
Familiarity with GIS software and computational modeling tools.
Excellent verbal and written communication abilities.

Interested candidates should submit their applications, including a cover letter, CV, and any relevant project work or publications, to the following form:

Open Research Positions: Join Our Team!

Position 1: Non contact sensing using wearable AR inspection tools and deep learning method

Description:

UE-funded research - HE - Excellent Science - MSCA

Qualifications:

Please submit your application, including a cover letter, CV, and any relevant project work or publications, to the link below.

Position 2: Integrated drone systems for inspection and analysis of urban comunities and infrastructures.

Description:

UE-funded research - HE - Excellent Science - MSCA

Qualifications:

Interested candidates should submit their applications, including a cover letter, CV, and any relevant project work or publications, to the following form:

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