PhD Scholarships in Component Production: The University of Bolzano is offering two PhD scholarships funded by the European Union – NextGenerationEU, specifically under MD 360/2024. These scholarships are designed for outstanding candidates interested in cutting-edge research in anomaly detection in X-ray CT scans and developing recommender systems for sustainable sinter component production. Both projects promise significant industrial applications and advancements.
PhD Scholarships in Anomaly Detection and Sustainable Sinter Component Production
Designation
PhD Candidate
Research Area
- Anomaly Detection and Segmentation in X-ray CT Scans
- Recommender Systems for Sustainable Sinter Component Production
Location
University of Bolzano, Bolzano, Italy
Eligibility/Qualification
Mandatory Skills for Project C1:
- Strong background in computer vision and machine learning
- Proficiency in deep learning frameworks such as PyTorch
- Completion of relevant courses in computer vision and deep learning
- Solid programming skills
Desirable Skills for Project C1:
- MSc thesis in deep learning and computer vision
- Publications based on MSc thesis
Mandatory Skills for Project C2:
- Good programming skills
- Excellent communication and writing skills
Desirable Skills for Project C2:
- Interest in planning and conducting human subject experiments and interviews
- Knowledge of AI methods
Job Description
Project C1: Anomaly Detection and Segmentation in X-ray CT Scans
- Supervisor: Oswald Lanz
- Project Description: This project aims to develop, implement, and test computer vision and deep learning-based methods for anomaly classification and segmentation in CT scans. The research will build on the group’s expertise in video analysis and data-efficient deep learning on volumetric data. The goal is to enhance automated quality inspection in various industries, improving production efficiency and transparency. Data for testing will be provided by MICROTEC SRL, and the project will be supported by Covision Lab.
Project C2: Data Based Actions: A Recommender System for Sustainable Sinter Component Production
- Supervisors: Zanker Markus, Prof. Angelika Peer
- Project Description: This project focuses on developing a recommender system to assist setters in sinter component production. The system will monitor compaction press behavior in real-time and propose actions to reduce inefficiencies, conserve resources, and facilitate training. The goal is to achieve a more sustainable, resource-efficient production process.
How to Apply
Interested candidates should prepare the following documents:
- Detailed CV
- Cover letter outlining research interests and relevant experience
- Academic transcripts
- Contact information for two references
Applications should be submitted through the University of Bolzano’s online application portal.
Last Date for Application
Applications must be submitted by August 31, 2024.
For more information and to apply, please visit University of Bolzano’s official website.
This PhD opportunity offers a unique chance to work on impactful research projects with industrial applications, supported by a leading university and industry partners. Don’t miss this chance to contribute to technological advancements and sustainable practices in key industries.
