The primary objective of the postdoctoral project is to investigate the relationship between metabolic changes in plants and their overall health conditions and stress levels. This will be achieved through the utilisation of data obtained from various omics and optical sensor techniques. A key aspect of the project will involve the fusion of data from different sources and the application of machine learning techniques.
Publications: Urano D (2012) Nature Cell Biology, Bradford W (2013) Science Signaling, Urano D (2016) Science Signaling, Wu TW (2021) Nature Plants, Wu TW (2022) Molecular Plant, Leong R (2023) New Phytologist, Koh SS (2023) Plant Phenomics. The full publication list can be found at https://www.tll.org.sg/group-leaders/urano-daisuke/.
- Design and develop machine learning models specifically tailored for analysing Next Generation Sequencing (NGS) data, metabolome data, and optical sensor data.
- Conduct metabolic modeling and perform in silico perturbations of metabolic pathways to gain insights into plant metabolism under different conditions.
- PhD degree in bioinformatics, biostatistics and/or computer science
- At least one first-author research paper in an international peer-reviewed journal
- Knowledge in NGS or other large-scale biology data analysis would be advantageous
- Excellent communication skills and research ethics are highly required
Salary and benefits are commensurable to educational qualifications and working experience of the candidate. Benefits include annual leave, medical and flexi-benefits, etc.
Interested individuals should send their applicants (including a cover letter, curriculum vitae with list of publications and three referees’ contacts/ recommendation letters) to:
Dr URANO Daisuke
Temasek Life Sciences Laboratory Ltd
(Please note that only shortlisted candidates will be notified)