PhD Students in Computer Vision for Physiological Monitoring
Develop non-intrusive video-based methods to estimate human breathing and pulse for health and cognitive monitoring.
Best for:Best for computer vision and machine learning researchers with a strong background in deep learning and video analysis.
Overview
KTH Royal Institute of Technology invites applications for PhD positions in computer vision to monitor human physiological parameters such as heart rate, breathing patterns, and skin temperature from video feeds. These non-intrusive measurements provide insight into underlying emotional and cognitive states like anxiety, concentration, and depression without relying on traditional intrusive clinical equipment. The selected candidates will work within the School of Electrical Engineering and Computer Science in collaboration with perceptual neuroscience researchers at Karolinska Institutet. Successful candidates will design novel deep learning and computer vision architectures, conduct rigorous evaluations, and contribute to interdisciplinary scientific literature.
What you'll do
- Develop innovative video-based algorithms to monitor human physiological parameters including heart rate, respiration, skin temperature, and pupil dilation
- Design and program deep neural networks for advanced video analysis and feature extraction
- Collaborate closely with perceptual neuroscience researchers at Karolinska Institutet on clinical and cognitive applications
- Analyze complex physiological and visual datasets to infer underlying human cognitive and emotional states
- Publish research findings in top-tier computer vision and machine learning venues
- Pursue doctoral coursework and independently drive research objectives toward a doctoral degree
Requirements
Degree required: Master's degree with a major in Machine Learning, Artificial Intelligence, Signal Processing, Computer Vision, or equivalent second-cycle qualification
Degree offered: PhD
Language: English equivalent to English B/6
Instruction: English
- Documented experience and proficiency in machine learning, specifically programming deep neural networks
- Documented experience in computer vision, image processing, or signal processing
- Ability to independently pursue research work and solve complex technical problems
- Strong collaborative skills for working within multidisciplinary teams
- Passed a second cycle degree (such as a master's degree) or completed course requirements of at least 240 higher education credits with at least 60 second-cycle credits
Funding & terms
How to apply
Interested applicants should submit their application through the official KTH online recruitment portal by following the application link and providing all required qualification documents.
- CV
- Master's degree certificate and transcripts
- Cover letter
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