Research Fellow - AI for Life Sciences Applications

About the role

The School of Materials Science and Engineering (MSE) at NTU is seeking a Research Fellow in Artificial Intelligence for Aquaculture Applications. The successful candidate will lead research applying AI to challenges in aquaculture and drug delivery, at the interface of materials science, biology and computational modelling.

Key responsibilities

• Lead and execute AI-driven research projects in aquaculture, such as behavioural analysis, growth prediction, digital twins and computer vision.
• Develop, train and validate advanced computational models and machine learning algorithms tailored to complex datasets.
• Collaborate with multidisciplinary teams including biologists and engineers to turn AI methods into practical solutions.
• Publish research outcomes in top-tier journals and present findings at international conferences.
• Contribute to grant applications and proposals to secure external funding.
• Mentor graduate students and junior researchers in computational and AI research methods.
• Maintain computational resources, datasets and documentation to ensure reproducibility and integrity.
• Support collaborations with external academic, industry and government partners.

Required profile

• PhD in Computer Science, Artificial Intelligence, Data Science, Computational Biology or a related discipline.
• Strong research expertise in machine learning, deep learning or computational modelling with demonstrated application in life sciences or aquaculture.
• Proven track record of high-quality publications in international journals or conferences.
• Experience handling complex, multi-modal datasets (biological, imaging, environmental).
• Ability to work independently and collaboratively in a multidisciplinary environment, with strong analytical, problem-solving and communication skills.
• Prior experience in grant writing, project management or student mentoring is advantageous.

Required skills

• Programming in Python
• Familiarity with machine learning frameworks such as TensorFlow, PyTorch and Scikit-learn
• Image segmentation models such as YOLO-seg and SAM