Job Description
Dr Chen Jinmiao is looking for a motivated Research Associate to join the Centre for Computational Biology (CCB) who will participate in AI-powered analysis of single-cell and spatial omics for precision medicine. Dr Chen's research team develop AI algorithms for spatial and single-cell omics, integrates publicly available data into a centralised hub, and utilises this platform to build AI models for discovering biomarker and drug targets.
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, conceptualize 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.
