Job Description

Dr Anand JEYASEKHARAN's laboratory located at Cancer Science Institute of Singapore is seeking a highly motivated and skilled Bioinformatician with expertise in spatial transcriptomics and image analysis to support research on the immune response to chemotherapy in lymphoma. The successful candidate will work closely with cancer biologists, pathologist, and clinical collaborators to analyse high-dimensional spatial transcriptomic data and develop computational tools to interrogate immune modulation pathways in lymphoma samples.

Key Responsibilities

• Data Processing & Analysis: Process and analyse spatial transcriptomics data from platforms like 10x Genomics Visium, Xenium, Nanostring CosMx, and GeoMx.
• Image Analysis & Computational Workflow Development: Develop and apply computational methods for cell segmentation, phenotyping, and tumour microenvironment analysis. Implement deep learning and machine learning approaches for automated image processing of multiplex-immunohistochemistry (mIHC).
• Bioinformatics Pipeline Development: Design, optimise, and maintain bioinformatics pipelines using programming languages like Python and R.
• Statistical and Machine Learning Analysis: Apply spatial statistics and machine learning models to analyse gene expression patterns, tissue organization, and cell-cell interactions. Use graph-based and clustering algorithms to classify cell types and tissue structures.
• Biological Interpretation & Hypothesis Generation: Collaborate with researchers and clinicians to generate hypotheses based on spatial transcriptomics data. Perform pathway enrichment analyses and investigate tumour microenvironment heterogeneity and immune interactions.
• Data Visualization & Reporting: Create high-quality visualizations of spatial data using tools like Seurat, Scanpy, and ggplot2.
• Collaboration & Communication: Work closely with multidisciplinary teams, including pathologists, oncologists, and computational scientists. Present findings in research meetings, conferences, and contribute to grant applications and manuscripts.
• Database Management & Data Sharing: Manage large spatial transcriptomics datasets with proper metadata annotation. Ensure compliance with data-sharing guidelines and deposit datasets in repositories.
• Keeping Up with Emerging Technologies: Stay updated with advancements in spatial transcriptomics, bioinformatics, and imaging technologies. Benchmark and implement new tools to improve data processing and analysis workflows.

Requirements

• PhD in Bioinformatics, Computational Biology, Data Science, Cancer Biology, or a related field.
• Strong proficiency in R and Python for bioinformatics and image analysis.
• Experience with spatial transcriptomics platforms (e.g. 10x Visium, Xenium, Nanostring CosMx, and GeoMx) and/or high-dimensional multiplex immunohistochemistry (e.g. Akoya PhenoCycler-Fusion).
• Expertise in image analysis tools such as QuPath.
• Experience with machine learning and deep learning techniques for image processing is preferred.
• Knowledge of cloud computing platforms such as AWS for large-scale data analysis and storage is preferred.
• Familiarity with immuno-oncology research is optional.
• Strong analytical and problem-solving skills.
• Ability to work effectively in a multinational and interdisciplinary team.
• Excellent communication skills, with a proven track record of writing scientific publications.