Research Fellow (AI-Assisted Anomaly and Stress Detection for Controlled Environmental Aquaculture) Full-time

Research Fellow (AI And Data Science In Controlled Environmental Aquaculture)

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.

In this job opening, we are seeking a highly motivated and visionary researcher to join our team focused on AI and data science in controlled environmental aquaculture. This role involves research and development in AI-assisted methods for anomaly and fish stress detection, aiming to demonstrate early disease warning platforms outperforms traditional approaches. 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 AI-assisted methodologies and tools for anomaly and stress pattern detection in aquaculture systems.

• Focus on multi-modal sensor data integration and AI models tailored for fish behaviour, health, and stress signal analysis.

• Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and early intervention alerts in aquatic environments.

• Integrate open-source and commercial aquatech tools (e.g., air and underwater imaging, biosensor platforms) to build proof-of-concept detection pipelines.

• Investigate and build robust data and AI agent pipelines for continuous learning and knowledge acquisition, including annotation strategies and knowledge graph development for aquaculture stress events.

• Design AI-guided decision-making pipelines to support early warning systems, predictive health management, and mitigation of stress-induced anomalies

• Co-develop benchmarking frameworks and stress test environments to evaluate model accuracy, false-positive rates, and real-time system responsiveness.

• Collaborate closely with international renowned biologists, aquaculture domain experts, sensor engineers, and AI developers to deploy and validate stress detection systems in operational environments.

• Publish research outcomes with the team in leading conferences and journals in AI, aquaculture, bio technology, and environmental monitoring.

• Contribute to white papers and strategic roadmaps to guide long-term development of intelligent aquaculture monitoring systems.

Job Requirements

• PhD in Computer Science, AI, Bioengineering, Electrical/Electronic Engineering, Environmental Informatics, or a related field with focus on intelligent systems or biological sensing.

• Proven experience with sensor integration, data acquisition systems, and embedded hardware prototyping, preferably in aquatic or environmental monitoring contexts.

• Strong understanding of real-time digital signal processing, control systems, and embedded AI for biological applications.

• Experience with AI/ML development frameworks, model deployment pipelines, and simulation environments for behavior or anomaly detection in living organisms.

• Proven expertise in machine learning for time-series, video, or biosignal analysis; familiarity with reinforcement learning, agentic AI, or domain-adapted LLMs is highly advantageous.

• Familiarity with fish physiology, aquaculture stress indicators, or environmental sensing systems is beneficial; prior work in biosignal processing or environmental anomaly detection is a plus.

• Track record of research publications in top-tier venues in AI, aquaculture, bioinformatics, or environmental monitoring is strongly preferred.

• Strong analytical and problem-solving abilities

• Demonstrated ability to work across interdisciplinary and diverse teams, ideally with experience in project planning and management

• Excellent collaborative and communication skills in English for research purposes

• Good communication and presentation skills, fluency in multiple languages is a plus

We regret that only shortlisted candidates will be notified.