Associate professor at UGent-imec. Lead of the PreDiCT research team of IDLab, UGent-imec. Coordinating the research on predictive maintenance and predictive healthcare.
Assistant professor at UGent-imec. Co-coordinating the research on predictive maintenance and predictive healthcare.
Postdoctoral researcher active on predictive healthcare projects. Supervised by Femke Ongenae and Sofie Van Hoecke.
PhD research on anomaly detection and remaining useful life prediction using machine learning. Supervised by Sofie Van Hoecke and Dirk De Schrijver.
PhD research on anomaly detection and root cause analysis for the eHealth and predictive maintenance domain, using (semantic) knowledge models in combination with both black and white-box machine learning techniques.Supervised by Femke Ongenae and Filip De Turck.
PhD research on semantic reasoning on data streams in complex IoT contexts. Supervised by Femke Ongenae and Filip De Turck.
PhD research on anomaly detection and remaining useful life prediction by using hybrid machine learning. Supervised by Sofie Van Hoecke and Guillaume Crevecoeur.
Machine learning research on different projects related to predictive maintenance. Supervised by Sofie Van Hoecke.
PhD research on hybrid machine learning and the potential of wearables for improved migraine management. Supervised by Koen Paemeleire and Sofie Van Hoecke.
PhD research on predictive maintenance. Supervised by Sofie Van Hoecke and Bruno Volckaert.
PhD research on fixed and mobile sensors for feature and event detection in smart homes (fixed sensors) and outdoors (grassland - UAV footage/mobile sensors). Supervised by Sofie Van Hoecke and Peter Lambert.
PhD research on effective time series data wrangling applied to wearables (and speech) for user behavior modelling in ambulatory settings. Supervised by Sofie Van Hoecke.
PhD research on using wearables for user behavior modelling and predictive healthcare. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD research on hybrid and explainable AI for the Internet of Things (IoT) domain. Supervised by Femke Ongenae and Filip De Turck.
PhD research on Industry 4.0 and cloud infratructures to bring data from the sensor to the dashboard. Supervised by Sofie Van Hoecke and Bruno Volckaert.
PhD research on the fusion of semantics and machine learning for predictive healthcare. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD research on context-aware anomaly detection in network data. Baekeland PhD in collaboration with Skyline Communications. Supervised by Sofie Van Hoecke and Dennis Dreesen (Skyline Communications).
Senior developer and postdoctoral researcher working on the dynamic dashboard back-end. Supervised by Femke Ongenae.
PhD research on hybrid machine learning for antibiotic therapy management in the intensive care. Supervised by Jan De Waele and Sofie Van Hoecke.
Machine learning research on different projects related to predictive maintenance. Supervised by Sofie Van Hoecke.
PhD research on hybrid machine learning for antibiotic therapy management in the intensive care. Supervised by Sofie Van Hoecke, Jan De Waele and Femke Ongenae.
Machine learning research on different projects related to predictive healthcare. Supervised by Sofie Van Hoecke.
PhD research on the design and development of an AI-driven digital pathology device. Supervised by Sofie Van Hoecke, Bruno Levecke (veterinary science, UGent) and Lieven Stuyver (JnJ).
PhD research on Graph Convolutional Networks for predictive maintenance and healthcare. Supervised by Sofie Van Hoecke and Femke Ongenae.
Developer on our dynamic dashboard and mBrain app, as well as dashboard cluster management. Supervised by Sofie Van Hoecke.
PhD research on hybrid machine learning for predictive maintenance and/or healthcare. Supervised by Sofie Van Hoecke and Steven Verstockt.
PhD research on nove rule mining techniques to derive life pattern of people from knowledge graphs. Supervised by Femke Ongenae and Sofie Van Hoecke.
PhD research on deep patient representation using transformers for risk evaluation. Supervised by Sofie Van Hoecke and Femke Ongenae.
Educational supervisor for labs and projects in the Master of Science in Electronics and ICT Engineering Technology, and responsible for STEM. Supervised by Sofie Van Hoecke, Steven Verstockt, Francis wyffels and Johan Lauwaert.
We are looking for a junior researcher to contribute to (inter-)national research projects on machine learning and hybrid AI within preventive healthcare and/or predictive maintenance domains. You will cooperate with enthusiastic colleagues and diverse external partners to fulfill the project requirements while staying up to date with important changes in the related literature. Projects will allow you to collaborate with researchers and developers from Europe and beyond in the area of machine learning for preventive health and predictive maintenance. You will be given the opportunity to work on new machine learning technologies that will facilitate predictive modeling, anomaly and/or event detection, behavior modelling, .... You might work on both European and/or regional research projects depending of the topic. Experience with semantics, embeddings, graphs, ... is a plus.
We are also looking for a postdoctoral researcher to drive and perform research on machine learning and hybrid AI within preventive healthcare and/or predictive maintenance domains. As a postdoc, you will also help with following up research projects and PhD students, and with applying for research funding for the team.