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

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 50,000 - 70,000; Full time

Job summary

A Singaporean research institution is seeking a Postdoctoral Research Fellow in Artificial Intelligence and Machine Learning for spatial omics and precision medicine. The role involves developing new biomarkers and analyzing complex datasets. Desired qualifications include a Master's in a related field and proficiency in Python and R. Candidates with strong backgrounds in AI/ML and experience in metabolomics or multiplex tissue image analysis are preferred. The position offers collaboration opportunities in a dynamic environment.

Qualifications

• Master in Computer Science, Bioinformatics, or related field.
• Strong background in AI/ML, statistics, and data analysis required.
• Previous experience in metabolomics and/or multiplex tissue image analysis highly desirable.

Responsibilities

• Develop and apply novel AI and ML models for analyzing mass spectrometry data.
• Implement and benchmark computational algorithms.
• Present research findings at international conferences.

Skills

Proficiency in Python

Proficiency in R

AI/ML modelling

Statistics

Data analysis

Data visualization

Deep learning frameworks

Education

Master in Computer Science or related field

Tools

TensorFlow

PyTorch

Job description

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 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

• 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.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.