Master's thesis; AI for Smart Building Environments

Background

The next generation of smart buildings requires systems that are not just automated, but intelligent and responsive to human behaviour. Automatic doors are a key component of modern infrastructure, yet they often lack the intelligence to differentiate between intent and incidental movement, leading to unnecessary openings that waste energy and can disrupt pedestrian flow. In collaboration with ASSA ABLOY, the global leader in access solutions, RISE is launching a project to solve this challenge by creating an AI-powered entrance system that can understand and predict human intent.

Project Description

This Master's thesis project will focus on developing the core intelligence for a truly smart automatic door. The goal is to create an AI model that can accurately predict a person's trajectory and intent based on subtle visual cues. To achieve this, the student will work with a unique, large-scale video dataset collected at RISE, containing hundreds of recordings of real-world pedestrian movements.

The project involves a deep dive into computer vision and machine learning to build a system that can analyse a person's gait, posture, speed, and direction to determine whether they intend to pass through the doorway or are simply walking by. The result will be a more efficient, secure, and energy-conscious generation of building access systems.

Key Responsibilities

• Analyse and process a large-scale video dataset of pedestrian movement.
• Develop, train, and evaluate machine learning models for human trajectory and intent prediction.
• Investigate and implement feature extraction techniques from video streams to identify predictive signals.
• Rigorously benchmark the performance and accuracy of different modelling approaches.
• Collaborate with senior researchers at RISE and experts from ASSA ABLOY.
  
  
  

Qualifications

• Ongoing Master’s degree in Computer Science, AI, Data Science, or a related field.
• A solid background in AI, machine learning, and computer vision.
• Proficiency in Python and experience with ML frameworks like PyTorch or TensorFlow.
• Strong analytical skills and an enthusiasm for solving complex data-driven problems.
• A creative, analytical, and collaborative mindset.
  
  
  

What We Offer

• A chance to work on an impactful project that directly contributes to smarter, more sustainable buildings.
• Direct collaboration with ASSA ABLOY, a global industry leader.
• Access to a unique, large-scale video dataset for training and validation.
• Opportunities for professional growth and networking, including potential PhD opportunities.
• A friendly and dynamic research environment with experienced supervisors.
  
  
  

Terms

• Scope: The thesis usually comprises 30 credits (hp/ECTS).
• Start Date: Spring 2026, or by agreement.
• Location: RISE, Kista, Stockholm, with the possibility for some remote work.
• Compensation: In line with RISE guidelines for strategically important projects, a compensation of 39,990 SEK is offered upon completion and approval of the 30-credit thesis.
• Please note that due to industrial confidentiality and non-disclosure agreements, this position is restricted to EU citizens only.
  
  
  

Welcome with your application!

Applications must be submitted through the Teamtailor recruitment portal.

Last day of application: 27th of October, 2025.

For more information, please contact:

• Dr Fehmi Ben Abdesslem, [email protected]
• Dr Joakim Eriksson, [email protected]
• Miriana Passarotto, [email protected]