A postdoctoral research fellow position in the areas of Artificial Intelligence (AI) and Machine Learning (ML) for spatial omics and precision medicine is available at the Loo Lab in the Bioinformatics Institute (BII), A*STAR, Singapore. The group develops next-generation spatial omics assays, cellular phenotype analysis methods, and machine learning models to predict patient drug responses. The group also develops and manages the HistoPath Analytics (HPA) Platform and ImmunoAtlas (https://ImmunoAtlas.org) for automated management, visualization, and analysis of large multiplex tissue images and spatial multi-omics data.
The successful candidate will be part of an interdisciplinary team working on the development of new biomarkers and image analysis methods for cancer diagnosis and precision medicine. His/her main responsibility is to develop and apply novel AI and ML models and methods for analyzing large multiplex tissue images and spatial transcriptomic and metabolomic data collected from cancer patients. The candidate will also perform computational algorithm implementation and benchmarking, present research findings at international conferences, and publish in high-impact scientific journals. The candidate will have the opportunity to work in a highly stimulating environment, and collaborate closely with biologists, physicians, and pathologists. Senior candidate with relevant previous experience may also have the opportunity to plan and lead new projects.
Qualifications:
- PhD in Computer Science, Bioinformatics, Computational Biology, Molecular and Cell Biology, Cancer Biology, or a related field.
- Proficiency in Python and R programming languages.
- Strong background in AI/ML modelling, statistics, and data analysis and visualization. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) for image analysis and data modelling is required.
- Knowledge of molecular biology, cancer biology, and basic immunology is required.
- Previous experience in analyzing multiplex tissue images and spatial transcriptomics data is highly desirable.
- Good communication skills and fluent in both spoken and written, especially for the preparation of scientific manuscripts and reports.
- The ability to work effectively in a collaborative team setting.
