Postdoc in Artificial Intelligence for Enhanced Sorting and Plastic Recycling Catholic University of Leuven Department of Mechanical Engineering

The KU Leuven research group on Life Cycle Engineering (LCE) of the Department of Mechanical Engineering has in close cooperation with their industrial partners acquired significant experience in re- and demanufacturing, which includes reuse, repair, remanufacturing and recycling of various waste streams, as well as the dismantling of products into their components or composing materials. The KU Leuven has recently established the Re- and Demanufacturing Lab in Heverlee to support these research activities. The multidisciplinary ten-headed team working in this lab has in the framework of various national and international projects developed and installed various setups in this lab with state-of-the-art automated, spectroscopic, computer vision and ICT equipment for material characterization, robotic sorting and robotic dismantling. The research group ambitions to further develop their lab and infrastructure together with the related software and expertise in this field. This to support equipment manufacturers and recycling companies in developing the next generation of processes for sustainable demanufacturing and recycling and to support original equipment manufacturers in developing more sustainable (consumer) products in the ongoing transition towards a more circular economy. Responsibilities Description of the research topic The envisaged research will be part of the Horizon Europe project titled “Increasing recycled content in added-value products for a resilient and digitized circular economy” (INCREACE) (approved in the call HORIZON-CL4-2021-RESILIENCE-01). In this project KU Leuven will collaborate with in total 16 other European partners, of which companies (Philips, Erion, CABKA, Neste, Pezy Group, Plastika, Vorwerk Elektrowerke, Partners for innovation, Mirec, PAS, SAP, EGEN), research institutes (Fraunhofer IZM, Fraunhofer IVV, VITO) and other renowned universities (Maastricht University, ETH). In this project, KU Leuven will lead a “Data-driven plastic sorting, analysis and traceability” work package. The main objective of this work package is to enable a data-driven optimization of the sorting processes and higher traceability during the pre-processing of plastic recyclates. To achieve this, more detailed data is needed on the material composition and characteristics throughout the processing steps that are commonly adopted. Therefore, sampling procedures, artificial intelligence-driven sorting, and characterisation methods to define the composition of plastic recyclates during pre-processing will be developed. By validating the developments during experiments with state-of-the-art sorting equipment constructors (Redwave and Pellenc), KU Leuven aims to establish EU broadly accepted procedures to control the consistency and quality of recyclates for specific applications. A small multi-disciplinary team of researchers is foreseen to work on this project at the Re- and Demanufacturing Lab of the Lifecycle Engineering group of KU Leuven. Therefore, in the context of the INCREASE project: You will investigate how state-of-the-art deep learning technologies can be adopted in real-time in python or C++, using TensorTR, Onnx, MXNet and/or others. You will investigate the technical feasibility of integrating deep learning computer vision and data fusion technologies (e.g. RGB, NIR and 3D imaging) to distinguish different impurities from the plastics flakes and to extract valuable characteristics, such as the color and size distribution. You will, in parallel with these technological developments, be responsible for setting up practical sorting and sampling experiments with the different partners of the INCREASE project to validate the developed vision technologies throughout these experiments. You will develop novel testing procedures, obtain novel insights based on the obtained sampling results, and learn how well-established technologies could be combined with novel AI-based sorting technologies to improve plastic recycling. Description of the vacancy You are part of a multi-disciplinary research group working on the topic of Life Cycle Engineering (LCE) under the supervision of Prof. Jef Peeters. You follow-up literature, patents, company releases, conferences, etc., and use the obtained information to determine the state-of-the-art and identify opportunities for novel contributions to the field. You are responsible for developing, implementing, and evaluating real-time running convolutional neural networks for segmentation, classification and object detection of materials on a fast running conveyor and take the lead in organizing and performing validation experiments. You guide Master students with their thesis work. You report to your senior colleagues, supervisors, and the different partners during the bi-annual project meetings organized at the premises of the different project partners You present your research results at international conferences. As a postdoc (optional) you contribute to writing project proposals and in the guiding of junior colleagues in their research and publications. Profile You hold a Master’s in Science, Engineering, or equivalent degree, for which you obtained a high GPA (a cum laude level is a prerequisite). You have good programming skills and experience with Python or C++ or MATLAB programming You have experience with implementing computer vision, deep learning, machine learning and image processing algorithms. You like programming, but you also like to bring theory to practice by hands-on experiments. You like to work in a multi-disciplinary team of international researchers and show willingness to learn/explore innovative technologies and techniques. You have a creative mindset, like to take initiative, and are not afraid to address innovative ideas and new opportunities. You have good communication skills (oral and written) in English or are willing to improve your language skills. Offer We offer a varied and challenging job with an attractive salary package. We offer a research position in a stimulating and multi-disciplinary environment and a solid industrial network. We will support you in publishing in high-ranked international scientific journals. We provide the opportunity to work towards obtaining a PhD degree within an international network (optional). Interested? For more information please contact Prof. dr. Jef Peeters, tel.: +32 16 32 25 69, mail: jef.peeters@kuleuven.be. You can apply for this job no later than December 05, 2022 via the KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be. Employment percentage: Voltijds Location: Leuven Apply before: December 5, 2022 Tags: Werktuigkunde Download: file If you apply for this position please say you saw it on Computeroxy Apply

The KU Leuven research group on Life Cycle Engineering (LCE) of the Department of Mechanical Engineering has in close cooperation with their industrial partners acquired significant experience in re- and demanufacturing, which includes reuse, repair, remanufacturing and recycling of various waste streams, as well as the dismantling of products into their components or composing materials. The KU Leuven has recently established the Re- and Demanufacturing Lab in Heverlee to support these research activities. The multidisciplinary ten-headed team working in this lab has in the framework of various national and international projects developed and installed various setups in this lab with state-of-the-art automated, spectroscopic, computer vision and ICT equipment for material characterization, robotic sorting and robotic dismantling. The research group ambitions to further develop their lab and infrastructure together with the related software and expertise in this field. This to support equipment manufacturers and recycling companies in developing the next generation of processes for sustainable demanufacturing and recycling and to support original equipment manufacturers in developing more sustainable (consumer) products in the ongoing transition towards a more circular economy.

Responsibilities

Description of the research topic

The envisaged research will be part of the Horizon Europe project titled “Increasing recycled content in added-value products for a resilient and digitized circular economy” (INCREACE) (approved in the call HORIZON-CL4-2021-RESILIENCE-01). In this project KU Leuven will collaborate with in total 16 other European partners, of which companies (Philips, Erion, CABKA, Neste, Pezy Group, Plastika, Vorwerk Elektrowerke, Partners for innovation, Mirec, PAS, SAP, EGEN), research institutes (Fraunhofer IZM, Fraunhofer IVV, VITO) and other renowned universities (Maastricht University, ETH).

In this project, KU Leuven will lead a “Data-driven plastic sorting, analysis and traceability” work package. The main objective of this work package is to enable a data-driven optimization of the sorting processes and higher traceability during the pre-processing of plastic recyclates. To achieve this, more detailed data is needed on the material composition and characteristics throughout the processing steps that are commonly adopted. Therefore, sampling procedures, artificial intelligence-driven sorting, and characterisation methods to define the composition of plastic recyclates during pre-processing will be developed. By validating the developments during experiments with state-of-the-art sorting equipment constructors (Redwave and Pellenc), KU Leuven aims to establish EU broadly accepted procedures to control the consistency and quality of recyclates for specific applications.

A small multi-disciplinary team of researchers is foreseen to work on this project at the Re- and Demanufacturing Lab of the Lifecycle Engineering group of KU Leuven. Therefore, in the context of the INCREASE project:

You will investigate how state-of-the-art deep learning technologies can be adopted in real-time in python or C++, using TensorTR, Onnx, MXNet and/or others.
You will investigate the technical feasibility of integrating deep learning computer vision and data fusion technologies (e.g. RGB, NIR and 3D imaging) to distinguish different impurities from the plastics flakes and to extract valuable characteristics, such as the color and size distribution.
You will, in parallel with these technological developments, be responsible for setting up practical sorting and sampling experiments with the different partners of the INCREASE project to validate the developed vision technologies throughout these experiments.
You will develop novel testing procedures, obtain novel insights based on the obtained sampling results, and learn how well-established technologies could be combined with novel AI-based sorting technologies to improve plastic recycling.

Description of the vacancy

You are part of a multi-disciplinary research group working on the topic of Life Cycle Engineering (LCE) under the supervision of Prof. Jef Peeters.
You follow-up literature, patents, company releases, conferences, etc., and use the obtained information to determine the state-of-the-art and identify opportunities for novel contributions to the field.
You are responsible for developing, implementing, and evaluating real-time running convolutional neural networks for segmentation, classification and object detection of materials on a fast running conveyor and take the lead in organizing and performing validation experiments.
You guide Master students with their thesis work.
You report to your senior colleagues, supervisors, and the different partners during the bi-annual project meetings organized at the premises of the different project partners
You present your research results at international conferences.
As a postdoc (optional) you contribute to writing project proposals and in the guiding of junior colleagues in their research and publications.

Profile

You hold a Master’s in Science, Engineering, or equivalent degree, for which you obtained a high GPA (a cum laude level is a prerequisite).
You have good programming skills and experience with Python or C++ or MATLAB programming
You have experience with implementing computer vision, deep learning, machine learning and image processing algorithms.
You like programming, but you also like to bring theory to practice by hands-on experiments.
You like to work in a multi-disciplinary team of international researchers and show willingness to learn/explore innovative technologies and techniques.
You have a creative mindset, like to take initiative, and are not afraid to address innovative ideas and new opportunities.
You have good communication skills (oral and written) in English or are willing to improve your language skills.

Offer

We offer a varied and challenging job with an attractive salary package.
We offer a research position in a stimulating and multi-disciplinary environment and a solid industrial network.
We will support you in publishing in high-ranked international scientific journals.
We provide the opportunity to work towards obtaining a PhD degree within an international network (optional).

Interested?

For more information please contact Prof. dr. Jef Peeters, tel.: +32 16 32 25 69, mail: jef.peeters@kuleuven.be.

You can apply for this job no later than December 05, 2022 via the

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

Employment percentage: Voltijds
Location: Leuven
Apply before: December 5, 2022
Tags: Werktuigkunde
Download: file

If you apply for this position please say you saw it on Computeroxy

Apply