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.
PhD promotion December 2022. PhD topic was “Towards Machine Learning-Based Predictive Maintenance in Industry Using Vibration and Acoustic Data”. Doing research on the potential of AI for neuromodulation. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD promotion January 2023. PhD topic was “Knowledge Graph Creation and Embedding to Realize Explainable Hybrid AI Applications”. Doing research and project management on context-aware and hybrid AI for health. Supervised by Femke Ongenae and Sofie Van Hoecke.
PhD promotion August 2023. PhD research was on semantic reasoning on data streams in complex IoT contexts. Doing research and project management on behavioral monitoring apps, cloud and dynamic dashboards for health. Supervised by Femke Ongenae and Sofie Van Hoecke.
PhD promotion May 2023. PhD research was 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.
Doctoral researcher working on machine vision and applications. Supervised by Steven Verstockt and Sofie Van Hoecke.
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).
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 deep patient representation using transformers for risk evaluation. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD research on explainable AI for predictive healthcare. Supervised by Sofie Van Hoecke and Dirk De Schrijver.
PhD on personalized and engaging behavioral change interventions. Supervised by Femke Ongenae, Lieven De Marez (MICT, UGent) and Sofie Van Hoecke.
PhD research on stress monitoring and scalable cloud architectures. Supervised by Sofie Van Hoecke and Bruno Volckaert.
PhD research on personalized healthcare by enabling machine learning-driven agents across Solid pods. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD research on automatic detection of parasites in stool and blood smear images using trustworthy machine learning. Supervised by Sofie Van Hoecke, Wesley De Neve and Janarthanan Krishnamoorthy (Jimma Univ. Ethiopia).
PhD research on hybrid AI for neuromodulation and dermatology use cases. Supervised by Sofie Van Hoecke and Femke Ongenae.
PhD research on using wearables for user behavior modelling and predictive healthcare. 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.