KTH Royal Institute of Technology
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6 days ago
PhD students in Computer Vision to estimate breathing and pulse from video KTH Royal Institute of Technology in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Sep 27, 2026
Country
Sweden
University
KTH Royal Institute of Technology

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About this position
KTH Royal Institute of Technology is advertising two PhD positions in computer vision and machine learning focused on estimating breathing and pulse from video. The project is part of KTH’s third-cycle subject in Computer Science and aims to develop video-based methods for monitoring human physiological parameters such as heart rate, breathing pattern, skin temperature, and pupil dilation.
The research motivation is both scientifically and practically strong: physiological signals can help infer states such as concentration, anxiety, and depression, while current clinical and cognitive-scientific systems often rely on intrusive devices. The successful candidate will work on novel non-invasive computer vision approaches for physiological monitoring, with collaboration involving perceptual neuroscience researchers at Karolinska Institutet in Solna, Sweden.
The position is supervised by Hedvig Kjellström. The announcement is for doctoral study, and admission decisions are made as part of the recruitment process. Only candidates admitted to postgraduate education can be employed as doctoral students.
Eligibility is clearly defined: applicants must meet the general Swedish postgraduate admission requirements, such as holding a second-cycle degree or equivalent knowledge, and must also have a Master’s degree in Machine Learning, Artificial Intelligence, Signal Processing, or Computer Vision. A mandatory English requirement equivalent to English B/6 applies. Strong documented experience in machine learning, especially programming deep neural networks, is emphasized, together with documented experience in computer vision, image processing, or signal processing. Experience with medical applications and 3D analysis of humans in video is considered meritorious.
The employment is a temporary doctoral position at KTH, with salary according to KTH’s doctoral student salary agreement. The normal duration corresponds to four years of full-time doctoral education, and the role may include limited departmental duties. Two positions are available in Stockholm, Sweden.
To apply, candidates must use KTH’s recruitment system and submit all required documents: diplomas and grades, proof of language requirements, a CV, a short application letter describing research interests and career goals, and representative publications or technical reports. Applications must be complete and received by the deadline.
Application deadline: 2026-09-27.
Funding details
Available
What's required
Applicants must have basic eligibility for postgraduate education: a second-cycle degree (for example a master's degree), or at least 240 higher education credits with at least 60 second-cycle credits, or equivalent knowledge. The applicant must also have a Master’s degree with a major in Machine Learning, Artificial Intelligence, Signal Processing or Computer Vision. English proficiency equivalent to English B/6 is mandatory. Strong documented experience in machine learning, especially programming deep neural networks, and documented experience in computer vision, image processing, or signal processing are required. Experience with medical applications and 3D analysis of humans in video is meriting. Candidates should be goal oriented, persevering, able to work independently and with others, professional, and able to analyze complex issues.
How to apply
Apply through KTH’s recruitment system and admission process. Include diplomas and grades, proof of language requirements, a CV, a motivation/application letter (max 2 pages), and representative publications or technical reports with abstracts and links if applicable. Ensure all documents are complete and certified where required. Submit the application by the closing date at midnight CET/CEST.
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