Job Description:
Job Description The Computational Methods Developer will participate in research and development efforts aimed at solving problems in analyzing massive-scale biological data in particular genomics and spatial transcriptomics data with a mission of improving human health. The ideal candidate has a proven track-record in the development of computational methods for the analysis of biological data and expertise in areas such as machine learning statistics complex network data analysis or high-performance computing. The candidate joins a strong team of data scientists to work with has access to vast amounts of omics data and is encouraged to publish new methods and results in academic journals and conferences. The candidate will conduct research in method development and disease biology and must collaborate effectively with researchers in the Eric and Wendy Schmidt Center (EWSC) at the Broad Institute the Klarman Cell Observatory (KCO) MIT Harvard and beyond. This position is suited for a person who is excited by the prospect of learning adapting and applying modern machine learning and statistical techniques to solve the key challenges for emerging biological data modalities with revolutionary implications in advancing the state-of-the-art clinical practice. Responsibilities Adapting and applying existing computational methods to omics datasets Developing novel computational methods for understanding and organizing unstructured datasets with emphasis on spatial transcriptomics data Developing robust and generalizable inference algorithms that advance the state-of-the-art Writing well-crafted maintainable scalable and performant code Requirements Ph.D. in Computational Biology Computer Science Physics Math Statistics or related quantitative fields Strong communication skills and ability to collaborate with biologists computational biologists data scientists and software engineers on model requirements and design Scientific and numerical programming in Python or R Strong bash/shell scripting and proficiency with UNIX operating systems Familiarity with one object-oriented programming language (e.g. Java C++ Go) Preferred Skills 0-2 years post-Ph.D. experience working on machine learning or a related area Working knowledge of existing machine learning and probabilistic programming infrastructures (e.g. Theano TensorFlow PyTorch) Fluency with version control including distributed version control and Git in particular All Broad employees regardless of work location must be fully vaccinated for COVID-19 by Tuesday October 12 2021. Requests for exemption for medical or sincerely held religious beliefs will be considered. "All computational scientists at Broad are encouraged to continue developing their expertise by engaging with the wider computational community through Broad's vibrant Models Inference & Algorithms Initiative (broadinstitute.org/mia)."
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