ITEC is a research group of KU Leuven and imec, Flanders’ high-tech research and innovation hub for nanoelectronics and digital technologies.
The group conducts interdisciplinary research on the design, development, and evaluation of personalized and adaptive digital solutions, with applications mostly in the domain of technology-enhanced education and training, but also in media and in health.
In order to realize this, ITEC brings together researchers trained in the learning sciences, statistics, computer science, and applied linguistics, in a cooperative research lab on the Kulak campus in Kortrijk.
The research group often collaborates with industry and societal partners.The main focus of the AI and machine learning subgroup is to apply existing and develop new machine learning algorithms to advance the application domains.
always with a specific attention to interpretability.You have (or will acquire in the next few months) a master degree in computer science, informatics, statistics, artificial intelligence, or equivalent, with excellent ('honors'-level) grades.
You have followed courses related to data mining or machine learning, and show a significant enthusiasm for, and knowledge of, this domain.
Be sure to mention any knowledge of (or previous experience with) survival analysis, network mining or medical applications.
and on the other hand you are motivated, curious and creative to work on the more fundamental part of this project.You have excellent oral and written communication skills in English.
Knowledge of Dutch will be considered an important asset. You are able to communicate about your work to people with a different background.
You are able to work independently, but also to integrate and interact with a team of fellow PhD researchers.This PhD position,which is funded by the Research Foundation Flanders (FWO), consists of two main parts.
First, the candidate will carry out fundamental research on the intersection between interaction learning and survival analysis.
In particular, interaction learning - also called pairwise learning or network mining - is situated in the machine learning domain.
It deals with predicting or clustering interactions between two sets of objects (e.g. users and products). Survival analysis is rooted in statistics and deals with predicting the time until an event occurs.
Recently survival analysis has entered the machine learning field. The research group has acquired expertise in both domains, however the combination of interaction learning and survival analysis is novel.
The candidate will develop new algorithms for prediction and clustering in this context. Second, the developed algorithms will be applied in the context of developing smart alarms in an intensive care unit.
Given the current overload of alarms, the candidate will contribute to reduce alarm fatigue in clinical staff and alarm anxiety are sleeping disturbances in patients.
For this purpose, the candidate will work on real data from and in close collaboration with the local hospital.Besides doing research, the candidate is expected to participate in educational activities (participate in seminars, be involved in teaching, ).