Postdoc in Computer Vision directed towards human motion analysis

The goal of the project is to develop video-based methods for modeling human motion, with applications toward 3D pose estimation, activity recognition, and synthetic motion generation. The project includes data collection with motion capture equipment and programming of generative machine learning models.

Reference number: PA-2026-2883.

Job description

The goal of the project is to develop video-based methods for modeling human motion, with applications toward 3D pose estimation, activity recognition, and synthetic motion generation. The project includes data collection with motion capture equipment and programming of generative machine learning models.

Qualifications

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Research expertise, documented through publications in international conferences and journals in Computer Vision.

The applicant should have documented experience in machine learning, particularly programming deep neural networks. You should also have documented experience in computer vision, image processing, or signal processing, as well as experience with data collection using various motion capture systems.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Teaching abilities
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality
• Collaborative abilities
• Independence

Great emphasis will be placed on personal skills.

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.

To apply for the position

Log into KTH's recruitment system to apply for this position. You are responsible for ensuring that your application is complete according to the instructions in the ad.

The application must include

• CV including relevant professional experience and knowledge.
• Copy of diplomas and grades from your previous university studies. Translations into English or Swedish if the original documents have not been issued in any of these languages.
• Brief account of why you want to conduct research, your academic interests and how they relate to your previous studies and future goals. Maximally two pages long.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

Other information

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.

Contract details

• Number of positions: 1
• First day of employment: According to agreement
• Location: Stockholm, Sweden
• Contact: Hedvig Kjellström (hedvig@kth.se)

Apply via the official KTH vacancy page. Incomplete applications and applications sent by email are typically not considered unless the vacancy states otherwise.

Requirements

• A doctoral degree or an equivalent foreign degree. This eligibility requirement must be met no later than the time the employment decision is made.
• Research expertise, documented through publications in international conferences and journals in Computer Vision.

The applicant should have documented experience in machine learning, particularly programming deep neural networks. You should also have documented experience in computer vision, image processing, or signal processing, as well as experience with data collection using various motion capture systems.

Preferred qualifications

• A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
• Teaching abilities
• Awareness of diversity and equal opportunity issues, with specific focus on gender equality
• Collaborative abilities
• Independence

Great emphasis will be placed on personal skills.