Research Associate (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 position of Research Associate will focus on developing advanced high throughput phenotyping tools for assessing the performance of genotypes under abiotic and biotic stresses that occur in indoor farming systems for vegetables (e.g. too high humidity, too high VPD, low light intensity, low or high water potential in the root environment, salinity, bacterial and fungal diseases) and to optimize performance (yields of economic product in indoor cultivation) through breeding.

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  Develop high throughput phenotyping protocols (e.g. for testing nutrient deficiency responses, transpiration response, biomass accumulation, quality of produce) using automated determination of transpiration, imaging (side view imaging with RGB), 3D-imaging, chlorophyll fluorescence imaging.

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  Develop analysis tools for the large data stream of HTP phenotyping for assessment of abiotic and biotic stress tolerance (e.g. determine extent of damage to a disease or functional response to humidity); also involving use of AI in image analysis and predictive modelling

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  Develop prediction models that can predict product yield and quality from HTP data either for cultivation management or for predicting which genotypes have best performance in practice

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  Set up test of products (e.g. CRISPR or EMS mutants) from other work packages (e.g. with modified Engineer molecular pathways for enhanced crop performance

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  Document results and contribute to reports, publications, and presentations

• Master’s degree covering topics in Plant Biology, Bioinformatics, Data Science, Statistics, Image analysis

• Basic understanding of plant phenotyping and analysis of large data sets, preferably also using AI tools for image analysis

• Strong analytical skills and ability to work independently and as part of a team

We seek a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.