Graduate Co-op: AI/ML for Clinical Affairs

Thornton Tomasetti; Saint Paul (MN); On-site; USD 25,000 - 49,000; Full time

Benefits offered by this job

Health benefits with HIP PPO

Tuition reimbursement

FreeU education benefit

Strong retirement plan with employer 4

Job summary

Abbott’s Electrophysiology (EP) team in St. Paul, MN invites a PhD co-op candidate to develop AI/ML solutions for clinical outcomes, imaging, and trial enrollment forecasting.

You will work with clinical scientists to design, train, and validate predictive models using multimodal data from imaging, records, and procedures. This winter PhD co-op emphasizes data harmonization, feature engineering, and building reproducible prototypes.

Qualifications

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

Responsibilities

• 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.

Skills

Python

R

SQL

MATLAB

Julia

Git

Statistics

Cloud

Education

Master's or PhD in CS/Bioinformatics/Computational Biology/Health Informatics/Engineering

Tools

PyTorch

TensorFlow/Keras

Scikit-learn

XGBoost/LightGBM

GitHub

DICOM

Job description

Abbott’s Electrophysiology (EP) team in St. Paul, MN invites a PhD co-op candidate to develop AI/ML solutions for clinical outcomes, imaging, and trial enrollment forecasting.

You will work with clinical scientists to design, train, and validate predictive models using multimodal data from imaging, records, and procedures. This winter PhD co-op emphasizes data harmonization, feature engineering, and building reproducible prototypes.