Winter Graduate Co-op - Clinical Affairs AI​/ML

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries.

2027 Winter PhD Co-op – Artificial Intelligence & Machine Learning

As the PhD Co-op, Artificial Intelligence & Machine Learning you'll support the EP Clinical Affairs organization by developing AI-solutions, advanced predictive analytics and multimodal machine learning models using clinical trial, procedural, imaging, and outcomes data.

At Abbott, you can do work that matters, grow, and learn, care for yourself and your family, be your true self, and live a full life.

• Career development with an international company where you can grow the career you dream of.
• Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year.
• An excellent retirement savings plan with a high employer contribution.
• Tuition reimbursement, the Freedom 2 Save student debt program, and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree.
• A company recognized as a great place to work in dozens of countries worldwide and named one of the most admired companies in the world by Fortune.
• A company that is recognized as one of the best big companies to work for as well as the best place to work for diversity, working mothers, female executives, and scientists.

The Opportunity This position works out of our location in the Abbott’s Electrophysiology (EP) business in St. Paul, MN. In Abbott’s Electrophysiology (EP) business, we’re advancing the treatment of heart disease through breakthrough medical technologies in atrial fibrillation, allowing people to restore their health and get on with their lives.

• Design, develop, train, evaluate and fine-tune predictive models for clinical trial enrollment forecasting, clinical outcomes and other clinical applications.
• Evaluate model performance using clinically relevant endpoints and validation methodologies.
• Build data pipelines for harmonizing and curating diverse clinical and procedural datasets.
• Perform data quality assessments, feature engineering, and model-ready dataset creation.
• Integrate and analyze large multimodal structured or unstructured datasets including medical imaging, clinical records, procedural data, adverse event data, and clinical trial datasets.
• Develop data pipelines, algorithms, and visualization tools.
• Track metrics and document results to improve model accuracy.
• Apply statistical, predictive, and generative AI techniques.
• Collaborate with clinical scientists to identify clinically meaningful questions, endpoints, and model performance criteria.
• Prepare technical reports, presentations, and recommendations.
• Collaborate with cross-functional stakeholders.
• Support scientific abstracts and publications.

• Validated and functional predictive model(s) and reproducible prototype(s) with documented performance metrics and recommendations for future clinical or operational applications.
• Leadership presentation of findings and recommendations.
• Technical documentation supporting future development.
• Contribution to a scientific abstract or publication draft.

Currently enrolled in a Master's or PhD program in Computer Science, Bioinformatics, Computational Biology, Health Informatics, Engineering, or related field. PhD candidates preferred.

• Advanced proficiency in Python required; experience with R, SQL,…