Computational Scientist (Biomedical Datahub Division/ Research Data Integration)

About us:

Data science is an important component of biomedical and translational research, where data of multiple modalities are being constantly generated at unprecedented scale. The Research Data Integration group in the Biomedical Datahub Division of the Bioinformatics Institute (BII), A*STAR, aims to bridge the complexity of computational biology and data science with the needs of biologists and clinicians to drive biological discoveries and predict translational outcomes. One of our immediate challenges is to analyze and integrate multi-omics, imaging and clinical data generated by biomedical institutes in A*STAR, healthcare institutions and national initiatives in Singapore to improve the usability and interpretability of large-scale multimodal datasets of cancer, metabolic diseases, and other diseases. We seek motivated individuals to join us to push the potential of biomedical data in truly benefitting patients.

Job description:

We are developing data platforms in BII Biomed DAR, integrating healthcare data including clinical and research data, for various diseases including cancer, metabolic diseases and skin diseases. We are seeking a computational scientist to develop analytical, statistical and machine learning methodologies and workflows for analysis and integration of large-scale multi-dimensional omics (bulk, single-cell, spatial), perturbation screens, imaging and clinical data) for these data platforms. The candidate is expected to drive data science research to uncover underlying diseases biology, druggable targets and develop personalized medicine approaches. This position offers the candidate an opportunity to work closely with a team of clinicians, wet-lab scientists and computational biologists. He/She is also expected to drive high impact publications and manage large-scale projects, including data management.

Requirements:

• PhD in Bioinformatics, Computational Biology, Data Science, Computer Science, Mathematics, Engineering or a related field.
• Proficient programming skills (e.g. Python, R, RStudio, Jupyter Notebook, Shinyapps).
• Familiarity with Unix/Linux environment and cloud architecture (AWS).
• Experience in large-scale omics (bulk, single-cell, spatial) data analysis and/or AI/ML.
• A background in life sciences, biology or biomedical informatics is preferred.
• Knowledge in data standards and interoperability.
• Strong analytical and problem-solving skills.
• Excellent oral and written communication and presentation skills.
• Able to work independently, and as part of a team, with a positive and enthusiastic learning attitude.
• Competent project and data management and organizational skills will be very valuable.