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Job Description
A Bioinformatician position is available in Dr Jeyasekharan's lab to assist with the analysis of data generated through spatial analytics platforms, along with correlative biostatistics on annotated clinical data.
Dr Jeyasekharan's research aims to understand regulators of immune clearance of cancer cells following genotoxic therapy, with a focus on the use of multiplexed spatial imaging assays to define these phenomena in clinical samples of cancer. You may refer to https://csi.nus.edu.sg/researcher/anand-jeyasekharan/ for more information
Interested applicants should include the following documents in the job application
- Curriculum Vitae (CV)
- Summary of past research experience
- Description of long-term plans
- At least 2 referees including their name, contact information (email) and relationship to applicant
Duties and Responsibilities
- To process and analyse data generated from spatial analytics platforms e.g. Lunaphore Comet, Akoya PhenoCycler, NanoString Digital Spatial Profiling and STOmics Stereoseq, applied to cancer tissue.
- To perform general bioinformatics-related tasks including: Sourcing, downloading, organizing and analysis of microarray, RNAseq, scRNAseq, and spatial analytics data from publicly available databases.
- To assist the PI and laboratory personnel with biostatistical analyses correlating phenotypes of interest with clinical parameters such as stage, risk categorization, survival
- To harmonize multiple clinical datasets and perform metanalyses
Qualifications
- Master’s Degree in Biomedical Informatics/Bioinformatics/Biostatistics, or related area with relevant research experience and training.
- Must have experience and expertise with processing and analysis of data generated from spatial analytics platforms.
- Proficient in statistical techniques commonly used in biomedical research (including Regression Analysis, Survival Analysis and Multivariate Statistical Analysis)
- Proficient in programming languages for bioinformatics/ statistics (R, Python), and in machine learning approaches
- To be a team player with the ability to work collaboratively with other members of the lab.
