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AI-Assisted Medicine/Surgical AI Internship
Position Description:
Are you passionate about working on compelling, real-world problems? At BAGIL, our vision is to use data science and artificial intelligence to transform medicine and healthcare delivery into a data-driven and evidence- based discipline that is personalized for each individual patient.
We are seeking candidates for a 6-12 month internship in AI Surgery.
Candidates must have expertise in AI, machine learning, deep learning, statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory and data science methods used to analyze data. This position provides the opportunity to:
Work with knowledge surgeons, AI architects, bionformaticians, clinicians, and partner companies to develop and deploy applications to bring AI and analytic solutions to nontechnical users, often at the point of care.
Role & Responsibilities:
The Surgical AI intern will interact with vast amounts of clinical and non-clinical data (external data sources, digital, etc.). Interns will learn the AI Engineering lifecycle including data acquisition, transformation, data engineering, exploratory data analysis, feature engineering, algorithm development, and algorithm deployment into a relevant clinical and/or business context.
Interns are expected to help formulate analytical questions, perform hypothesis testing, algorithm optimization, algorithm deployment, and integrate feedback rapidly from clinical stakeholders. Candidate needs to have an open mind and ability to take concepts developed by other industries and apply them to healthcare. Candidates should be effective communicators and good team players. Candidates who have coursework, projects, and evidence of interest in data science (e.g. participating in Kaggle competitions) but lack industry experience are ideal for this position.
Qualifications:
If you are interested in transforming medicine and joining BAGIL please submit your resume/CV with cover letter.
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