PhD and Postdoctoral positions in single cell algorithms and data analytics:
Analysis of Human diversity in the Human Cell Atlas (HCA)
Humans are diverse, and this diversity influences the properties of our cells, as well as our developmental processes, and disease mechanisms, diagnoses, and treatments. The impact of population-specific genetic variation on disease risk and healthcare is widely appreciated, and this can contribute to health disparities. To build a single cell reference atlas suitable for diverse population groups, we seek to characterise the impact of genes and environment on molecular traits in cells from diverse individuals across the globe. Specifically, we aim to uncover the influence of genetics, ancestry, age and sex on human phenotypes in health and disease.
We are looking for team members who will develop cutting-edge AI/machine learning methods and pipelines to integrate and interrogate gene expression and regulatory profiles of >10 million cells from thousands of donors. Your work will help establish a healthy immune baseline for disease comparisons, and craft and curate atlases of human cells that will transform our understanding of human biology. Successful candidates for this position will have the opportunity to collaborate with, learn from, and contribute to teams across the world studying diverse donors and tissues, including the Human Cell Atlas bionetworks.
Requirements
Postdoctoral Fellows: Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data
We are looking for smart, motivated machine learning and data analytics researchers who can lead or contribute to the development of new spatial and single cell methods for diagnostics and drug target discovery. The candidate should have strong mathematical intuition and programming skills, and be comfortable working in a highly collaborative multidisciplinary environment that includes biologists, pathologists and medical oncologists. This will be a unique opportunity to work on new data types and ask new questions in the rapidly growing field of spatial omics.
This will be part of a multidisciplinary program that creates spatial and single cell genomic technologies, applies them to clinical samples, generates massive multimodal datasets and interprets them to infer the molecular drivers of tumour growth and response to immunotherapy and other treatments. We have a track record of combining cutting-edge computational and experimental approaches to infer disease mechanisms and develop clinical applications (Joanito et al., Nat Genet 2022 ; Li et al., Nat Genet 2017; Sun et al., Cell 2016; Fukawa et al., Nat Med 2016; Chen et al., Science 2015; del Rosario et al., Nat Methods 2015; Kumar et al., Nat Biotechnol 2013; Ku et al., Lancet Oncol 2012).
Requirements
Bioinformatics Specialists: Machine Learning and Genome Data Analytics
We are looking for smart, motivated machine learning and data analytics researchers who can contribute to the development of new spatial and single cell methods for diagnostics and drug target discovery. The candidate should have strong mathematical intuition and programming skills, and be comfortable working in a highly collaborative multidisciplinary environment that includes biologists, pathologists and medical oncologists. This will be a unique opportunity to learn cutting edge data analytics and computational biology techniques in the rapidly growing field of spatial omics.
This will be part of a multidisciplinary program that creates spatial and single cell genomic technologies, applies them to clinical samples, generates massive multimodal datasets and interprets them to infer the molecular drivers of tumour growth and response to immunotherapy and other treatments. We have a track record of combining cutting-edge computational and experimental approaches to infer disease mechanisms and develop clinical applications (Joanito et al., Nat Genet 2022 ; Li et al., Nat Genet 2017; Sun et al., Cell 2016; Fukawa et al., Nat Med 2016; Chen et al., Science 2015; del Rosario et al., Nat Methods 2015; Kumar et al., Nat Biotechnol 2013; Ku et al., Lancet Oncol 2012).
Requirements