Bioinformatics and Machine Learning Intern, Fall 2021 at Harmonic Discovery
About the role
Harmonic Discovery is looking for a bioinformatics intern. We are recruiting undergraduate and graduate students to train machine learning models, perform gene expression analysis and help create the next generation of therapeutics.
We offer a hands-on ML experience for interested computational biology students. You will work on huge datasets, find interesting patterns and develop bioinformatics tools.
Harmonic Discovery is committed to a multicultural environment and strongly encourages applications from women and underrepresented minorities.
Undergraduate or Graduate student enthusiastic about machine learning and drug discovery
Experience in R or Python programming languages
Experience in bash and shell scripting
Experience using computational biology libraries and databases such as Bioconductor, BioPerl, BioPython
Great communication skills and ability to communicate effectively using tools such as Powerpoint.
WHAT WE OFFER
Work from anywhere! Come by the office if you feel like it, or don’t. Your call. Feel free to work from anywhere in the US as long as you have a stable internet connection and can sync your calendar’s time zone with the team’s.
Experience start-up culture firsthand. You will work closely with the entire team, from the scientists to the CEO.
No busy work here, you’ll work on projects that have an immediate and obvious impact to the company.
A great culture. From free lunches to board game nights, even if you are remote (we will ship things to you). We are building a flexible and friendly work culture in the lab or at home.
Harmonic Discovery is an equal opportunity employer.
Harmonic Discovery recruits and employs regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law.
Why you should join Harmonic Discovery
We are a drug discovery company developing a new generation of therapeutics that embrace the complexity of disease. Currently available approaches for creating drugs work on the principal of finding one drug-one protein 'magic bullets'. However, diseases such as cancer and autoimmunity are often the result of several dysregulated proteins across many distinct biological pathways.
We are building a computational-experimental platform to design therapeutics that can target several disease-causing proteins at once. By designing multi-specific drugs, we are able to create therapeutics that are more efficacious and safer than existing medicines.