Research Fellow (CBDS / CJM)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 95,000; Full time

Job summary

The National University of Singapore (NUS) invites applications for a Research Fellow to join the Centre for Biomedical Data Science (CBDS). The role focuses on AI-powered analyses of single-cell and spatial omics to advance precision medicine, and to integrate public datasets into a central data hub for biomarker discovery and drug target identification.

Under supervision, you will develop AI-enabled tools, perform large-scale omics analyses, interpret complex datasets, and contribute to

Qualifications

• PhD in a related scientific area with hands-on experience in bioinformatics and genetics/genomics data analyses.
• Familiar with and experienced in handling omics datasets, including spatial transcriptomics, RNA-seq, and single-cell RNA-seq.
• Proficient in R / Python and Linux / Bash.
• Prior experience with additional computational platforms and applications (e.g. Quantum computing, Perl, Java) preferred.
• A team player who is able to prioritise, multi-task and collaborate in a diverse workforce.

Responsibilities

• Develop new computational tools applying AI, deep learning, machine learning, statistics to spatial and single-cell omics data.
• Independently perform computational and bioinformatics analyses for large-scale spatial and single-cell omics and NGS studies.
• Apply qualitative and quantitative research techniques to -omics datasets and conceptualise ideas for independent research.
• Write and review research papers and present outcomes.
• Carry out data curation and data backup tasks as required.
• Contribute to project management and supervise junior researchers and students.

Skills

R

Python

Linux

Bash

Bioinformatics

Machine learning

Education

PhD in a related field

Tools

R

Python

Linux

Bash

Job description

Job Description

Dr Chen Jinmiao is looking for a motivated Research Fellow to join her laboratory within the Centre for Biomedical Data Science (CBDS). The successful candidate will contribute to AI-powered analyses of single-cell and spatial omics to advance precision medicine. Dr Chen's research team develops AI algorithms for spatial and single-cell omics, integrates publicly available datasets into a centralised data hub, and leverages this platform to build AI models for biomarker discovery and drug target identification.

The selected candidate will perform a variety of research activities under the supervision of the Principal Investigator, including but not limited to the following:

• Develop new computational tools through the application of AI / deep learning / machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data.
• Independently carry out computational and bioinformatics analysis for large-scale spatial and single-cell omics as well as next generation sequencing studies.
• Application of qualitative and quantitative research techniques which include accurate in-depth assessment, interpretation, and evaluation of -omics data sets, conceptualise new ideas and develop plans for independent research in the field of single-cell and spatial omics and other -omics data modalities.
• Write and review research papers and present research outcomes.
• Perform routine duties related to the research study, such as data curation and making data backups, as required.
• Contribute to project management, provide guidance to junior researchers as well as undergraduate and graduate students, occasional educational/instructional activities.
• Perform other related duties incidental to the work described herein.

Job Requirements

• PhD in a related scientific area (e.g., AI, Deep Learning, Machine Learning, Statistics, Database, Bioinformatics, Computational Biology, Computer Science, Data Science, Data Analytics, Biology) with demonstrated hands on experience in bioinformatics and genetics/genomics data analyses.
• Familiar with and experienced in handling and manipulating omics datasets, including spatial transcriptomics, RNA-seq, and single-cell RNA-seq, etc.
• Proficient in R / Python and Linux / Bash.
• Prior experience and working knowledge of additional computational platforms and applications (e.g. Quantum computing, Perl, Java, etc.) preferred.
• A team player who is able to prioritise, multi-task and work collaboratively in a diverse workforce.

We regret that only shortlisted candidates will be notified.