At Sage Bionetworks, we believe that we can learn more by learning from each other. By improving the way scientists collaborate, we help to make science more effective. We partner with researchers, patients, and healthcare innovators to drive collaborative data-driven science to improve health. Making science more open, collaborative, and inclusive ultimately advances biomedicine.
We are looking for a Research Scientist to join our Neurodegeneration Research group. You will contribute to computational biology research projects within the National Institute on Aging’s MODEL-AD consortium – a consortium that is taking insights from our understanding of Alzheimer’s disease to build better mouse models. Research areas may include 1) inference of Alzheimer’s disease stage with manifold learning or other machine learning techniques, 2) developing methods to identify AD subtypes based on integrative data analysis across omic and clinical data, and 3) identification of early stage disease drivers, disease stage, and disease subtypes from single-cell RNA-sequencing data. Enthusiasm for the application of computational biology and technology to enable open, collaborative, and reproducible biomedical research is essential.
We’d love to hear from you if you
Have a Ph.D. in Computational Biology, Bioinformatics, Neuroscience, Computer Science, or related discipline; or another scientific degree with commensurate experience.
Have experience working with high dimensional genomic data, including sequencing data, gene expression, genotype, CNV, and/or data from other high-throughput biological technologies.
Demonstrate excellence in research with evidence of advancing an area of computational biology.
Are proficient in R or Python.
Understand advanced machine learning or statistical techniques, such as manifold learning, probabilistic graphical models, Bayesian inference, and optimization methods.
Have excellent written and verbal communication skills.
Have strong collaboration and teamwork skills.
Are passionate about open science, reproducible research, and collaboration.