Job Description

We are looking for a self-initiated, energetic research fellow to join the group with background and interest in computational biology and handling large biological/molecular data-sets. He will work with a cross-disciplinary team to build and develop guided models for nutrients-nutraceuticals resource partitioning for specialized pathways and metabolic networks of interest targeting plant phytochemicals and plant-microbiome environment relationships.

He/She will be required to use a combination of genome-scale metabolic models and AI/ML driven models of biomolecules to understand source-sink relationships of metabolites of interest. Source-sink partitioning of metabolites across different scales of plant biological organization (e.g., cellular level, tissue level and organ level) will allow us to understand (a) metabolic network organization and (b) emergent metabolic network structure. We will use machine-learning techniques to build data-driven models which in turn can inform emergent structure and produce predictions. Using this two-pronged approach, we will be gaining a multi-level understanding of specialized pathways, decrypt missing components and key switches which regulate such pathways. This project aims to apply machine learning and systems biology approach to understand interactions and effect of soil and endophytic microbial communities on the selected South-East Asian crop plants. The candidate will develop predictive models for crop growth, metabolism and selected nutrient quality.

He/She will be expected to work with other team players and actively support research for other teams as and when required based on project requirements. He will be expected to plan and execute his tasks independently and contribute towards peer-reviewed publications in internationally recognized journals.

Qualifications * Qualifications / Discipline: Applicant should have a doctoral degree in life sciences, engineering, computing or mathematical sciences or related fields.

• Skills: Artificial intelligence, machine learning such as Scikit-learn, TensorFlow and Keras is desirable. python or R programming.
• Experience: Relevant working experience in generating, processing, and interpreting systems-biology datasets demonstrated through peer-reviewed research publications. 1-3 years of working experience in developing and interpreting either process-based or phenomenological models, demonstrated through at least 3 peer-reviewed research publications.

Covid-19 Message

At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.

Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.

More Information

Location: [[Kent Ridge Campus]]
Organization: [[National University of Singapore]]
Department : [[Department of Biological Sciences]]
Employee Referral Eligible: [[No]]
Job requisition ID : 17709

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