PhD Student

Last application date
Jan 15, 2024 00:00
Department
TW08 - Department of Electromechanical, Systems and Metal Engineering
Contract
Limited duration
Degree
Master's degree in Physical Sciences or Engineering (Materials Science, Physics, Chemistry) or Information Sciences/Engineering (Computer Science, Electrical Engineering, Mathematics).
Occupancy rate
100%
Vacancy type
Research staff

Job description

  • At least 90% of your assignment will be spent on academic research in preparation of a doctoral dissertation.
  • This is a position within the Horizon Europe project AID4GREENEST (https://aid4greenest.eu/), an ambitious initiative dedicated to advancing the development of environmentally sustainable steels through cutting-edge AI-driven characterization techniques.
  • It presents a unique opportunity for a highly motivated candidate with a passion for machine learning and materials science to engage in transformative research. The successful applicant will undertake computational tasks central to the project's goals:

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1. Employing explainable AI (xAI) and deep learning image captioning models to automate the interpretation of steel microstructure images.
2. Utilizing both machine learning and deep learning (encompassing supervised and self-supervised computer vision) to predict steel properties and processing methods.
3. Using Generative AI to create synthetic microscopy images starting from processing parameters, thereby enhancing the performance of other models.
4. Developing, refining, and analyzing a public database of microscopy images (including SEM, EBSD) and associated data, leveraging materials insights alongside AI methods like visual anomaly detection and material space visualization.

Job profile

- The ideal candidate will have completed a Master's degree in Physical Sciences or Engineering (Materials Science, Physics, Chemistry) or Information Sciences/Engineering (Computer Science, Electrical Engineering, Mathematics). Interdisciplinary skills are highly valued and will be further developed throughout the PhD program.

  • Candidates should ideally possess expertise in one or several of the following domains:
  • Proficiency in Python programming and experience in machine learning and computer vision.
  • Familiarity with command-line interfaces, particularly within high-performance computing environments (Linux).
  • Background in materials science or metallurgy, with a specific focus on steel microstructures and processing methods.
  • Experience in microscopy image characterization and analysis (OM/SEM/EBSD).

This fully funded, multi-disciplinary PhD position spans four years and bridges the fields of materials science and artificial intelligence. As part of this role, you will join both the Department of Electromechanical, Systems and Metal Engineering (EA08) and the Center for Molecular Modeling (CMM) at Ghent University. Collaborative opportunities with industry AI-partner ePotentia, steel company OCAS NV, and other AID4GREENEST project partners will enrich your research experience.

We invite applications from top-tier students eager to contribute to a greener future through innovative research at the intersection of AI and materials science.

WHAT WE CAN OFFER YOU

  • We offer a full-time position as a doctoral fellow, consisting of an initial period of 12 months, which - after a positive evaluation, will be extended to a total maximum of 48 months.
  • Your contract will start on 1 February 2024 at the earliest.
  • The fellowship amount is 100% of the net salary of an AAP member in equal family circumstances. The individual fellowship amount is determined by the Department of Personnel and Organization based on family status and seniority. A grant that meets the conditions and criteria of the regulations for doctoral fellowships is considered free of personal income tax. Click here for more information about our salary scales
  • All Ghent University staff members enjoy a number of benefits, such as a wide range of training and education opportunities, 36 days of holiday leave (on an annual basis for a full-time job) supplemented by annual fixed bridge days, bicycle allowance and eco vouchers. Click here for a complete overview of all the staff benefits (in Dutch).

How to apply

Send an email to stefaan.cottenier@ugent.be that contains A4GH20 in the subject line, with as attachment a package with pdf documents (one or more files), containing the following info:

  • your motivation letter (please address the topics mentioned in the job description and job profile)
  • your CV
  • a transcript of the required degree (if already in your possession), preferably with some comments (e.g. what does a given grade or rank mean in your country or institution).
  • A link to a short video (3 minutes) where you explain a scientific topic of your choice to a general audience. This video can be shot using a handheld smartphone.
  • Name and address of two referees who may be contacted to provide their perspective on your qualifications and suitability as a candidate.
  • optional: reference letters or testimonials