Postdoc in Computer Vision and Smart Sensing Aluminium Recycling

Leverage computer vision, smart sensing, and AI approaches to advance aluminium recycling and accelerate the transition to a circular economy.

Aluminium is one of the world's most widely used metals, and its recycling plays a vital role in sustainable metallurgy and a circular economy. However, current aluminium recycling processes face significant challenges, particularly in recovering and sorting aluminium scraps from complex and heterogeneous waste streams. High-quality recycling increasingly depends on advanced sensing technologies that can accurately characterise scrap properties and identify alloy types, enabling more efficient downstream metallurgical processing and improved metal circularity.

Over recent decades, numerous sensing technologies have been developed for metal scrap characterization. Yet, robust and cost-effective solutions suitable for industrial deployment remain limited. Next-generation recycling systems will combine multiple sensing techniques (e.g., optical sensing, spectroscopy, and electromagnetic sensing) with artificial intelligence (AI) to robustly deliver high-quality recycled aluminium for promoting circularity.

The Engineering Structures (ES) Department at TU Delft is seeking a motivated Postdoctoral Researcher to advance smart sensing and AI technologies for sustainable aluminium recycling as part of the Horizon Europe CLEANMAT (Smart Clean Aluminium Material Processing) project. CLEANMAT brings together seventeen partners from eight countries, spanning universities, research and technology organisations, industrial companies, SMEs and industrial associations. The project aims to establish a low-emission, end-to-end pathway for converting challenging scrap streams into high-performance aluminium alloys.

As a postdoctoral researcher, your primary responsibilities will be:

• Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and classification of aluminium scraps and contaminants.
• Design and validate algorithms for multi-sensor data interpretation and sensor fusion.
• Conduct laboratory experiments on scrap characterisation and sorting.
• Collaborate closely with TU Delft researchers and CLEANMAT consortium partners through regular meetings and joint activities.
• Prepare project deliverables, technical reports, and reputable journal publications.

Your host group is the Section of Resources & Recycling (R&R) at TU Delft. Research in the R&R group is renowned for translating scientific research into industrial reality, resulting in about half a dozen spin-out companies to date. As a postdoc researcher in our group, you will have access to advanced sensing and computational facilities at TU Delft and collaborate with leading European universities, research institutes, and industrial partners involved in the CLEANMAT project across Europe.

The role offers opportunities to contribute to innovation development, high-impact publications, and the dissemination of results across Europe.

The Department of Engineering Structures focuses on the development of resilient, smart and sustainable structures and infrastructures. Our aim is to meet societal demands in transportation, the energy transition and sustainable reuse. Research themes include dynamics of structures, mechanics of materials related to e.g., climate change, modelling and design of railway systems, multi-scale modelling of pavement materials and structures, reuse of materials, structures and parts of structures, assessment methods for structures, smart monitoring techniques, design methods, replacement and renovation of civil infrastructure and development of new materials and maintenance techniques.

Our unique Macro Mechanics Laboratory facilities support full-scale testing, monitoring and modelling of structures to facilitate implementation of innovations. The department delivers both groundbreaking research and world-class education for undergraduate and graduate students. As a team, we represent different backgrounds, skills and views. We foster an inclusive culture, as our combined identities, attitudes and ambitions widen our perspective and make up our strengths.

• A PhD degree in Civil, Mechanical, Automation/Electrical Engineering, Applied Mathematics/Mechanics, Computer Science or related areas.
• Demonstrated experience in one or more of the following areas (1) AI (particularly…