Summary: We are looking for an Intern within our Artificial Intelligence team who is keen on contributing to critical projects that eventually can improve dental health of millions of people.
About us: VideaHealth is a venture-backed startup developing artificial intelligence to automatically detect diseases in dental x-ray imaging. Spun out of MIT in 2018, our prior research has shown that dentists miss up to 50% of dental diseases. VideaHealth helps dentists detect these conditions and effectively communicate treatment recommendations to patients. Our product increases revenue for dentists and has the potential to reduce health risks for over 210 million patients every year in the US alone. We have a strong core team and advisors, are backed by world-renowned VCs and angel-investors, and our work has been featured at MIT and the Wall Street Journal. We have great momentum and are now looking for ambitious talent to join our team!
About the position: This Internship position is for at least 3 months with flexible start time and can be set up remote, if circumstances require. You will be part of the AI team at VideaHealth, where you will closely work within the team of Machine Learning engineers on a well defined project that is focused, dependent on the candidate, around Deep Learning or Data Science. We are looking for a curious candidate with a background in Statistics, Data Science, Computer Science who wants to have an impact on human health. You will learn from a diverse team of experts while being challenged with applied, real-world technical problems involving the development and implementation of state of the art algorithms and the analyses of large datasets.
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VideaHealth is transforming Dentistry by combining advanced AI of x-ray images with integrated software that dramatically improves diagnoses and streamlines insurance claims processing. Patients get better recommendations, dentists learn faster, and insurers reduce fraud, waste and abuse in real time. Our AI factory continuously expands the range of conditions we can detect, including those that affect broader medical risks. Our UI and integratio