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Doctoraatsbursaal/phd Student Active Learning for Solid Waste Characterization

International students are invited for a PhD scholarship at University of Ghent. Applicants should apply before January 31, 2019.

Vito and UGent are looking for candidates for a PhD research position on active learning for solid waste characterization. The selected candidate will be on Ghent University’s payroll with the goal of obtaining a PhD Degree. The actual research will take place mostly at the Vito site in Mol.

Currently, waste characterization is still often done manually. This approach is slow, subjective, expensive, unpleasant and eventually it delivers only little information. There is a need for a fast, objective and automated method that delivers data on a much more detailed level. Therefore VITO started the development of a multi-sensor characterization device and the required machine learning algorithms. Installed sensors include dual energy x-ray transmission, 3d laser triangulation and a high resolution color camera.

Currently, the algorithms have been trained to successfully recognize about a dozen of material types within mixed waste streams. Algorithm training was performed by feeding the device "pure" material streams, of which many were manually prepared to ensure correct labeling of the data. Not surprisingly, this preparation of "pure" (mono-material) streams turned out the be a major challenge in the training process of a waste characterization device.

Therefore VITO wants to investigate how to develop a semi-supervised learning framework that can learn to classify waste particles with a minimum of labeled particles, while still learning from the similarities between unlabeled particles. This approach is known as active learning. During the training process, the system should classify particles in existing classes and suggest new classes to the user. For this, interactive visualization tools should be developed that allow the user to explore and interact with the particle similarity that the algorithm calculates (e.g. t-SNE).

Candidates should send a CV, a detailed transcript of courses, and where possible a pdf of scientific documents (master thesis, scientific papers) they have written and which may help us to assess their scientific attitude. They should also add a motivation letter.

Applications should be sent towilfried.philips@UGent.be and to roeland.geurts@vito.be.


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