Research Associate (Visual and Biosignal Data Analytics for Animal Stress Detection and Animal Health Monitoring)

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.

Research Associate, Data Analytics for Animal Stress Detection and Early Warning Systems in Controlled Environmental Aquaculture

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 AI related to risk assessment aquaculture health and stress detection.

You will focus on the experimental development and deployment of multimodal animal stress detection and 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 Associate (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 behavior recognition, data analytics, and biosignal interpretation (e.g., growth rate estimation, movement, coloration).

• Design and implement experiments for detecting stress markers using underwater cameras, and other sensors and modalities in industrial environments (RAS farm)

• Integrated validate pipelines for fish biometrics, posture and movement analysis, feeding behavior monitoring, and associated data analytics.

• Investigate and integrate physiological, behavioral and environmental data from diverse IoT environmental sensors for multi-modal stress detection together with the team.

• Perform 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 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:

• MSc in Computer Science, AI, other fields such as Cyber Security related to risk assessment and early warning systems, or a related field with emphasis on biosignal analysis and multi-modal data analytics systems, early warning or foresight

• Proven experience in data analysis, data processing, or physiological signal interpretation.

• Strong foundation in AI, deep learning and machine learning frameworks; experience with video annotation tools and datasets.

• Hands-on experience with experimental setups.

• 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.

• Excellent communication skills; experience working in multi-stakeholder, cross-cultural environments is a plus.

We regret that only shortlisted candidates will be notified.