Research Fellow (Plant Biology and Bioinformatics)

The School of Chemistry, Chemical Engineering and Biotechnology (CCEB) at NTU Singapore, in collaboration with Wageningen University & Research (WUR), is part of Singapore Indoor Farming System (SingFarm)—a CREATE-NRF programme focused on building a sustainable, data-driven food production model with strong industry relevance to contribute Singapore’s ambition in achieving 20 per cent local production of its fiber (vegetables, mushrooms etc.) by 2035. The candidate would be working in a multinational team at the forefront of Singapore’s agricultural innovation and food security.

The Research Fellow will focus on developing and applying advanced high-throughput phenotyping tools to assess genotype performance under abiotic and biotic stresses relevant to indoor farming systems for vegetables. These include environmental stresses such as high humidity, high vapour pressure deficit (VPD), low light intensity, water stress, salinity, and disease pressures caused by bacterial and fungal pathogens. The role will contribute to the development of predictive models and breeding strategies aimed at improving crop performance, yield, and quality in indoor cultivation systems.

Key Responsibilities:

• Develop and implement high-throughput phenotyping (HTP) protocols to evaluate plant responses to environmental and biotic stresses, including nutrient deficiencies, transpiration dynamics, biomass accumulation, and produce quality, using automated phenotyping platforms, RGB imaging, 3D imaging, and chlorophyll fluorescence imaging.

• Analyze and interpret large-scale phenotyping datasets to assess plant stress responses and tolerance traits. This includes applying image analysis techniques, machine learning, and AI-based approaches for trait extraction, stress detection, and predictive modelling.

• Develop and validate predictive models linking HTP-derived traits with crop yield and quality outcomes, supporting both cultivation management decisions and genotype selection for indoor farming systems.

• Design, conduct, and manage controlled-environment experiments to evaluate plant materials developed through other project work packages (e.g., CRISPR-edited lines, EMS mutants, or other genetically improved germplasm) for enhanced crop performance.

• Work closely with multidisciplinary and international project partners to integrate phenotyping, breeding, and data analytics approaches within the SingFarmS programme.

• Document research findings and contribute to project reports, scientific publications, conference presentations, and grant-related deliverables.

Job Requirements:

• PhD Degree in Plant Biology, Plant Science, Crop Science, Bioinformatics, Data Science, Statistics, Image Analysis, or a closely related discipline.

• Demonstrated experience in plant phenotyping, particularly in assessing abiotic and/or biotic stress responses, with strong quantitative and data analysis skills.

• Experience in analyzing large and complex datasets, preferably using machine learning, AI-based image analysis, or predictive modelling approaches.

• Proven ability to independently conduct research, analyze and interpret data, and publish findings in peer-reviewed journals.

• Experience in controlled-environment agriculture, indoor farming, plant breeding, plant phenotyping, or related research areas would be advantageous.

• Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary and multinational research environment.

We regret that only shortlisted candidates will be notified.