Doctoral Researcher, Multimodal AI and Physiological Sensing for Cardiometabolic Health
About this role
Faculty of Medicine, Health Sciences and Technology
The University of Oulu is a multidisciplinary, international research university, with about 4000 employees who produce new knowledge based on high-standards research and provide research based education to build a more sustainable, smarter, and more humane world. The University of Oulu community has about 19,000 people in total. Our northern scientific community operates globally and creates conditions for the emergence of innovations. The University of Oulu is ranked in the top 3% of the world's universities.
We are now looking for a
Doctoral Researcher
to join a multidisciplinary research environment spanning two faculties at the University of Oulu:
the Research Unit of Health Sciences and Technology (HST), Faculty of Medicine
the Center for Machine Vision and Signal Analysis (CMVS), Computer Science and Engineering, Faculty of Information Technology and Electrical Engineering
The position offers a distinctive opportunity to conduct research at the intersection of artificial intelligence, signal processing, computer vision, and digital health. The objective of the position is to carry out high-quality doctoral research and to complete a doctoral degree during the four-year employment period.
About the job
The position is part of the Research Council of Finland-funded Academy Research Fellow project led by Dr. Xiaoting Wu: CARDIAB: Towards Cardiometabolic Intelligence-Decoding Heart Dynamics for AI-Augmented Diabetes Monitoring and Human Sustainable Wellbeing.
As a doctoral researcher, you will:
Conduct doctoral research on multimodal physiological sensing and AI-based analysis of cardiac dynamics.
Process and analyze ECG, PPG, camera-based physiological measurements, and radar signals.
Contribute to the design of data collection protocols, sensor synchronization, data annotation, and research data management.
Evaluate associations between multimodal physiological patterns, diabetes-related factors, and