项目介绍

2025年2月13日

Job description

PhD Researcher in trustable artificial and computational intelligence

The Database, Document and Content Management research group is seeking a highly motivated and talented PhD student to join Flanders AI Research Program. Our team focuses on developing new technologies for handling veracity in computational and artificial intelligence with diverse applications in decision making, criterion handling and machine learning.

Topic
The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data and data quality.
This task aims to further develop the logic framework for confidence handling that has been built in FAIR 1.0. In FAIR 1.0, the focus was on (i.) data quality assessment ,(ii.) studying confidence assessment and (iii.) the introduction of so-called L-grades as a means to express both satisfaction of criterion evaluation and confidence in the evaluation results. In FAIR 2.0 propagation of confidence measurement in logic reasoning and computations will be studied. Novel aspects to be developed are in the areas of aggregation, ranking, thresholds and fusion.
• With respect to aggregation, so-called sibling aggregators will be studied. A sibling aggregator is meant to aggregate L-grades. First the satisfaction grades are aggregated using an (advanced) computational intelligence (CI) aggregator. Second the confidence grades are aggregated in such a way that each confidence grade contributes to a similar extent to the computation of the overall confidence grade as its corresponding (sibling) satisfaction grade contributes to the computation of the overall satisfaction grade. CI aggregators that will be studied include ordered weighted average (OWA), and generalized conjunction/disjunction (GCD) as these are commonly accepted to adequately reflect human reasoning.
• Ranking concerns the study on how to rank L-grades. Depending on the context the user might give priority to satisfaction over confidence or vice versa, or look for the best balance between satisfaction and confidence, or look for the best option that can be obtained for a given cost.
• Thresholding is among others, important in view of optimization and flexible query answering reduction or enrichment because it impacts on which L-grades (and their corresponding objects) are kept into consideration for further computations.
• Fusion relates to the research question on how to compute an overall score from a (satisfaction, confidence) pair. Such a score can help to make the logic framework broader applicable in AI.

The task also aims to obtain better insight in how to integrate the developed logic framework in hybrid computational intelligence / machine learning approaches. For that purpose, a use case handling the measurement of the confidence of the results obtained from (semi-)automated AI based pseudonymization of texts.
The research will also be contextualized in two industry relevant use case scenarios.

Role and responsibilities
This is a PhD project that will be executed in close cooperation with researchers of the AI Flanders Research program and researchers of the DDCM research group.

Your primary tasks is to perform fundamental and applied research on the subject described above with the objective of achieving a PhD in Computer Science Engineering. This includes writing high quality publications, targeting top journals and international conferences.

In addition to your primary research responsibilities, you will actively contribute to the educational mission of our institution by providing support for various courses in the area of database management.

Job profile

Job profile

We are looking for a highly creative and motivated PhD student with the following qualifications and skills:

• You have (or will obtain in the next months) a European master’s degree in computer science, Artificial Intelligence, or equivalent, with excellent (‘honors’-level or above) grades.
• You have a strong background in information management and/or artificial intelligence.
• You are willing to work in a multidisciplinary context• Previous experience with active inference, reinforcement learning or other ML-based techniques for control is considered a plus but is not a necessity
• You have excellent computer science skills (python, git, linux, etc.) .
• You have strong analytical skills to interpret the obtained research results.
• You are a team player and have strong communication skills.
• Your English is fluent (C1 CEFR level), both speaking and writing.

Our offer

• We offer the opportunity to do this research in an international and stimulating environment. The research will be conducted at the premises of DDCM Lab, located in Ghent.
• The selected candidate will be offered 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.
• 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 .

How to apply

How to apply
Send your application by email to Prof. Guy De Tré (guy.detre@ugent.be). Applications should include:

• An academic/professional resume
• Transcripts of study results
• A short overview describing your earlier research or technical work (e.g., scientific papers, link to GitHub repository, master thesis, report on project work, etc.). These documents need not be on the topic of the advertised position.

After a first screening, selected candidates will be invited for an interview (also possible via Teams). The selection process will involve multiple steps.