Research Fellow (Visual and Biosignal Data Analytics for Animal Stress Detection and Animal Health Monitoring) Full-time

Research Fellow Position At Fraunhofer-NTU Research Centre

The Fraunhofer-NTU Research Centre Lab is an autonomous research centre of NTU and Fraunhofer Society, focusing on developing technically advanced solutions in the areas of applied and translational research, quantum technologies, artificial intelligence, advanced communications and cybersecurity capabilities.

The work will be in joint collaboration with the NRF CREATE programme Singapore Aquaculture Solution Centre SAS-C, HUJ, NUS, A*STAR and industrial partners.

We are seeking a dedicated and innovative researcher to join our translational research team at the Fraunhofer-NTU Research Centre as part of the NRF CREATE programme Singapore Aquaculture Solution Centre (SAS-C). This position is tailored for candidates with expertise in visual signal processing, biosignal interpretation, growth monitoring, and integrated IoT system development for aquaculture health and stress detection.

You will focus on the experimental development and deployment of visual and physiological monitoring systems, supporting the creation of robust early warning and stress classification pipelines in close collaboration with fish farming industry R&D. This role complements ongoing work in AI-based decision-making and is ideal for candidates who combine field experimentation with advanced data analytics and system integration.

We invite applicants to join us as a Research Fellow (full-time). You will be part of the Fraunhofer-NTU Research Centre in collaboration with the NRF CREATE programme Singapore Aquaculture Solution Centre SAS-C.

Key Responsibilities:

• Research and develop methodologies and practical tools for fish stress and health assessment through visual behavior recognition, image-/video-based analytics, and biosignal interpretation (e.g., growth rate estimation, movement, coloration).
• Design and implement experiments for detecting visual stress markers using underwater cameras, and other imaging modalities in industrial environments (RAS farm)
• Develop and validate pipelines for fish biometrics, posture and movement analysis, feeding behavior monitoring, and associated visual analytics.
• Investigate and integrate physiological and behavioral data from diverse IoT environmental sensors for multi-modal stress detection together with the team.
• Lead sensor deployment experiments in controlled and semi-controlled aquaculture environments; optimize and calibrate and validate sensor streams for real-time analytics.
• Prototype and test AI-assisted pipelines for early detection of stress events using real-world video and sensor datasets.
• Collaborate closely with aquaculture operators, sensor developers, and AI researchers to translate findings into operational use cases.
• Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking.
• Support the development of translational “lab-on-farm” demonstrators for on-site aquaculture pilots, including sensor placement strategies and field data validation in Singapore.
• Publish and present research outcomes in high-impact journals and conferences in bioengineering, aquaculture systems, and computer vision.

Job Requirements:

• PhD or Senior Scientist in Bioengineering, Computer Vision, Environmental Technology, Biomedical Engineering, Aquatic Biology, or a related field with emphasis on biosignal analysis and imaging systems.
• Proven experience in visual behavior analysis, video processing, or physiological signal interpretation, preferably in aquatic or veterinary contexts.
• Strong foundation in computer vision and machine learning frameworks (e.g., PyTorch, TensorFlow, OpenCV); experience with video annotation tools and datasets.
• Hands-on experience with experimental setups involving camera systems, IoT sensor networks, or embedded sensing platforms.
• Experience designing and conducting experiments in aquaculture, animal science, or environmental biology is strongly advantageous.
• Working knowledge of sensor fusion, time-series analysis, or edge-computing in environmental monitoring applications. Working knowledge of sensor calibration and data quality control and validation
• Demonstrated ability to work in interdisciplinary teams involving engineers, biologists, and field practitioners.
• Strong problem-solving and systems thinking mindset; practical, application-oriented approach to R&D.
• Track record of publications and/or contributions to applied or experimental projects with measurable outcomes.
• Excellent communication skills in English; experience working in multi-stakeholder, cross-cultural environments is a plus.

We regret that only shortlisted candidates will be notified.