Postdoctoral Fellow/Research Officer in AI/ML for Spatial Omics (Complex Cellular Phenotype Analysis

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

A leading research institution in Singapore is offering a postdoctoral research fellow position focused on Artificial Intelligence and Machine Learning in spatial omics for cancer precision medicine. The role involves developing AI models for analyzing complex data and collaborating with interdisciplinary teams. Candidates should hold a PhD or Master's in relevant fields and have strong programming skills in Python and R, along with a background in AI/ML and data analysis. This position promises a stimulating research environment with opportunities for publishing results in high-impact journals.

Qualifications

• PhD or Master in Computer Science, Bioinformatics, or related field.
• Proficiency in Python and R programming languages.
• Strong background in AI/ML modelling and data analysis.

Responsibilities

• Develop and apply novel AI and ML models for data analysis.
• Implement computational algorithms and perform benchmarking.
• Collaborate with biologists and physicians on cancer diagnosis.

Skills

Python programming

R programming

AI/ML modelling

Statistics

Data analysis

Data visualization

Deep learning frameworks

Education

PhD or Master in relevant fields

Job description

Job Summary

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 (https://ImmunoAtlas.org) for automated management, visualization, and analysis of large multiplex tissue images and spatial multi omics data.

Responsibilities

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. He/she’s 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 work in a stimulating environment, collaborating closely with biologists, physicians, and pathologists. Senior candidates with relevant previous experience may also 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 analysing 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.