Keenfinity
Security and Communication Solutions
ResearchIntern–MultimodalComputerVision(RGB–ThermalFusion)
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“Research Intern – Multimodal Computer Vision (RGB–Thermal Fusion) at Keenfinity. Skills: Computer vision, Deep learning, Image fusion, RGB-Thermal Fusion. Work with image/video datasets. Understand baseline pipelines”
Industry & Context.
Solve real-world challenges
What They're Looking For.
Must Have
Experience with Python, Deep learning frameworks experience, Foundation in computer vision, Foundation in deep learning, Fluent in English
Nice to Have
Experience with multi-modal learning, Experience with image fusion, Experience with low-light vision, Familiarity with image registration, Familiarity with calibration, Familiarity with optical flow, Familiarity with feature matching, Experience with experiment design, Experience with ablation studies, Experience with evaluation protocols, Experience with reproducibility best practices, Interest in AI for vision systems
What You'll Do.
Work with image/video datasets
Understand baseline pipelines
Reproduce baseline pipelines
Design evaluation framework
Implement evaluation framework
Implement fusion algorithms
Benchmark fusion methods
Validate improvements
Prepare results package
How You'll Work.
Team & Collaboration
International team; Collaborative team
Communication Scope
Publication-ready results
Full Job Description
At Keenfinity, we are a globally leading provider of innovative and professional security and communication solutions. With over 4,200 employees in over 50 countries worldwide, our ambition is clear: we offer more than just technology – we secure, connect, and amplify the moments that matter in life. Next to our passion for technology we’re very passionate about our work environment. Based on values such as trust, appreciation, and accountability we all work together to shape the future – boldly, customer-focused and with a strong team spirit. As an intern, you will work on advanced multi-modal computer vision problems, focusing on improving visibility and robustness in low-light environments by combining RGB and thermal imaging. * Get hands-on with data – Work with visible (RGB) and thermal image/video datasets, including internal and public sources * Build strong foundations – Understand and reproduce baseline pipelines for image registration and fusion * Define how success is measured – Design and implement a robust evaluation framework, including both quantitative metrics and visual assessment * Develop fusion algorithms – Implement and benchmark baseline RGB–thermal fusion methods * Push innovation with AI – Design and train a reliability-aware fusion model using techniques such as attention or gated fusion * Handle real-world challenges – Improve robustness for imperfect alignment and low-visibility scenarios using signals like alignment confidence and illumination conditions * Detect and reduce artifacts – Develop methods to identify and minimize issues such as ghosting, hallucinations, and image distortions * Compare and validate results – Evaluate your approach against baselines using performance metrics and qualitative insights * Explore real-world impact (optional) – Validate improvements on downstream tasks such as object detection in low-light conditions * Communicate your findings – Document your work in a thesis/report and prepare a publication-ready
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