Postdoctoral Fellow/Research Officer in AI/ML for Spatial Omics (Complex Cellular Phenotype Analysis Group), A*STAR BII Full-time

Postdoctoral Fellow/Research Officer in AI/ML for Spatial Omics

A postdoctoral research fellow or research officer 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 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 metabolomics data 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 mass spectrometry data (LC/MS and MSI), large multiplex tissue images, and spatial transcriptomics 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 or Master in Computer Science, Bioinformatics, Computational Biology, Chemical Engineering, 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.

Basic knowledge of molecular biology, cancer biology, metabolism, and immunology is required.

Previous experience in analyzing metabolomics and/or multiplex tissue image is highly desirable.

Good spoken and written communication skills, especially for the preparation of scientific manuscripts and reports.

The ability to work effectively in a collaborative team setting.

Type of Employment: Full-Time

Minimum Experience: 2 Years

Work Location: Matrix