Job Description
We are looking for talented and dynamic Computational Biologists/Bioinformaticians/Data Analysts to work with a multidisciplinary group of scientists on the discovery of new targets for therapy in Diabetic Nephropathy (DN), a devastating complication of diabetes causing significant morbidity, mortality and human suffering. In Singapore, the incidence of DN as a cause of end-stage kidney disease is among the highest in the world. The job will be part of the NMRC-funded project called Diabetes Study in Nephropathy and other Microvascular Complications II that is coordinated by Duke-NUS Medical School, and will be based in within the Centre for Computational Biology (CCB) of Duke-NUS. The multi-omics data for the project have been already generated and are ready for analysis. The job is available immediately, and it will be for a period of 3 years, with possible extension depending on performance and funding availability.
The selected candidate will perform a variety of research activities within the overall scope of the DYNAMO II research project under the supervision of the Principal Investigator or his/her designate, including but not limited to the following: -
- Perform tasks and support all aspects of the research project.
- Assist researchers in programming and data analysis.
- Perform analysis of complex and large-scale genomics data, including (but not limited to) WGS and transcriptomic data (including single cell sequencing data).
- Use computational tools and algorithms written in one/all R, PERL and Python.
- Contribute to project management, presentations and publications of research work.
- Provide technical support and establish work procedures to ongoing research projects.
- Perform other related duties incidental to the work described herein.
Job Requirements
- Bachelor’s / Master’s Degree in a related scientific area (e.g., Bioinformatics, Computational Biology, Data Analytics) with demonstrated hands on experience in bioinformatics and genetics/genomics data analyses. Candidates with higher credentials may be considered for Research Associate appointment.
- Experience in data analysis and statistical modelling of genomic data, as well as in network analysis and application to complex diseases.
- Familiar with high-performance computing (HPC) environment and possesses good computational biology skills (experience in R, Perl, Python, SQL, Bash etc).
- 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.
