Bioinformatics Summer Internship – GRAIL – Apply Online
This 10-week summer intern position will be part of the Bioinformatics and Data Science (BXDS) team working at the interface of our one-of-a kind DNA methylation-based dataset of >10,000 cell-free DNA samples, outstanding cloud computing infrastructure, and unparalleled curated clinical data to discover novel biological insights into the detection of early stage cancer. You will be trained in the development of methods, analysis, pipelines, and algorithms of next-generation sequencing data, for cancer detection, conformance to regulatory requirements. You will enjoy life in the San Francisco Bay Area! You will participate with other summer interns, and in our scientific excellence and innovation culture!
Bioinformatics Summer Internship, Hybrid (on-site and remote)
Location: MENLO PARK, CA /RESEARCH & DEVELOPMENT – BXDS /CONTRACT/ HYBRID
YOUR BACKGROUND SHOULD INCLUDE
- Pursuing or recently completed a Masters or Ph.D. in Bioinformatics, Computational Biology or related field.
- Previous Life Science or Machine Learning research experience.
- Experience with programmatic workflows for manipulating, analyzing, and visualizing very large and disparate data sets..
- Experienced with statistics as applied to the analysis of high-dimensional biological data and analytical study design.
- Demonstrated understanding in at least one programming language (Java/C++, Go, Python, R,) and proficiency in Linux environment.
BONUS SKILLS
- knowledge of cloud technologies (such as AWS), experience with source control practices and tools (Perforce, Git, Arcanist, etc.), familiarity with TensorFlow, experience with R package + Shiny app development.
- The expected, full-time, annual base pay scale for this position is $107,000 – $137,000. Actual base pay will consider skills, experience, and location.
DAY-TO-DAY RESPONSIBILITIES
- Work on complex computational problems requiring exploratory data analysis, use of custom and existing bioinformatics tools, and application of various statistical and machine learning methods.
- Find new solutions to open-ended problems through collaboration with software engineers, data scientists, and bench scientists.
Exercise judgment in defining methods, techniques and evaluation criteria for obtaining results. - Translate findings into recommendations for the development of new products.
Possible areas of focus: Apply machine learning methods to help model and interpret biological data, identify and validate genomic features driving tumor development, develop and implement new or existing tools for visualizing novel genomic datasets to help drive bioinformatics and data science efforts.
Bioinformatics Summer Internship
