The Institute for Digital Molecular Analytics and Science (IDMxS), officially launched on 1 December 2022, is a Research Centre of Excellence (RCE) supported by a total investment of around S$160 million over 10 years. IDMxS’ overarching approach is to go beyond the neat simplicity of intermolecular interactions in dilute media and embrace the inherent complexity of biological interactions and dense heterogeneity of their environments. For more details, please view https://idmxs.org/.
We are looking for a Research Associate (RA) who will drive the expansion of COSMOS across IDMxS. The RA will be primarily embedded at NTU (with reporting to Duane at NUS) to facilitate direct interaction with data-generating groups, while maintaining regular engagement with the COSMOS development team at NUS. This co-embedding model ensures both access to domain-specific data and continuous development of the underlying framework.
Key Responsibilities:
Responsible for applying and adapting COSMOS to new datasets,
Working closely with domain experts to interpret discovered motifs, and co-developing mechanistic hypotheses.
Help guide data structuring and labeling strategies, contribute to experimental planning by identifying what data is needed to test hypotheses, develop reusable pipelines that can be deployed across multiple domains.
Requirements:
MSc degree in AI for Science (preferred), with a Bachelor’s degree in Science (e.g., Physics or Biology) or Engineering (e.g., Electrical, Computational).
Practical experience with unsupervised and self-supervised learning workflows on datasets from scientific instrumentation (e.g., microscopy, spectroscopy, time-series)
Strong scientific programming background in ML stack (i.e., Pytorch) and experienced in HPC environment.
We regret to inform that only shortlisted candidates will be notified.
Hiring Institution: NTUSkills Required
- Bachelor's degree in Science (e.g., Physics or Biology) or Engineering (e.g., Electrical, Computational)
- MSc degree in AI for Science
- Practical experience with unsupervised and self-supervised learning workflows on scientific instrumentation datasets (microscopy, spectroscopy, time-series)
- Strong scientific programming background in ML stack (i.e., PyTorch)
- Experience working in an HPC environment
- Ability to work with domain experts to interpret motifs, co-develop hypotheses, and design data labeling/structuring strategies
What We Do
The Lee Kong Chian School of Medicine (LKCMedicine) trains doctors with a focus on patient-centered care, integrating precision medicine, Artificial Intelligence (AI) in healthcare, and medical humanities into its undergraduate medical degree program.
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