2027 Winter Graduate Co-op - Clinical Affairs AI/ML

United States - Minnesota - St. Paulmid

OtherClinical

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Quick Summary

Overview

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,

Technical Tools

OtherClinical

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.

What We Offer

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✓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.

Requirements

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Advanced proficiency in Python required; experience with R, SQL, MATLAB, Julia, or other analytical programming languages preferred.
Experience with software development, version control, and code documentation.
Familiarity with core machine learning concepts, statistics, and frameworks
Experience developing machine learning or advanced analytical models.
Experience using Git for version control and familiarity with shell-based or cloud development environments.
Knowledge of statistical analysis, predictive modeling, and data mining.
Experience managing and analyzing large datasets.
Strong problem-solving abilities and a demonstrated eagerness to research and learn new AI technologies independently.

Experience with deep learning, NLP, generative AI, or large language models. Familiarity with libraries such as PyTorch, TensorFlow/Keras, Scikit-learn or XGBoost/LightGBM preferred.
Experience with multimodal AI, foundation models, or fusion methods combining imaging and structured clinical data.
Established and shareable GitHub repository
Experience working with cardiac CT, cardiac MRI, echocardiography, fluoroscopy, DICOM data, or other cardiovascular imaging datasets.
Experience with longitudinal data analysis, survival analysis, risk prediction modeling, or time-to-event methodologies
Publication record or demonstrated research excellence.