Graduate Co-op: Medical Imaging Data Scientist

Philips International; Plymouth (MA); Presencial; USD 94 558 000 - 137 540 000; Tempo integral

Resumo da oferta

Philips is offering a Graduate Level Co-op – Medical Imaging Data Scientist in Plymouth, MN. The 6–9 month paid program focuses on data-driven workflows for clinical imaging software, collaborating with AI/ML engineers and software teams to improve data quality, model performance and regulatory compliance.

The role includes hands-on data processing, model training, and an end-of-summer presentation detailing methods, results, and impact within a HealthTech environment.

Qualificações

• Pursuing a master’s or doctorate (PhD preferred) in CS, CE, or related field.
• Experience with deep learning frameworks (PyTorch, TensorFlow) and CNNs (UNet, ResUNet).
• Strong understanding of medical imaging standards/formats (DICOM/NIfTI) and data management tools.

Responsabilidades

• Support data gathering, processing and analyzing clinical imaging datasets.
• Assist with exploratory data analysis to identify patterns and data quality issues.
• Help validate data sources for AI/ML model development and evaluation.
• Document data sources, preprocessing steps, assumptions, parameters, and performance metrics.
• Assist model training/evaluation under guidance of senior engineers.
• Complete required regulatory training and present end-of-summer project results.

Conhecimentos

Graduate student

PyTorch

TensorFlow

CNNs

Data analysis

Team collaboration

Formação académica

MS/PhD candidate

Ferramentas

Python

PyTorch

TensorFlow

Descrição da oferta de emprego

Philips is offering a Graduate Level Co-op – Medical Imaging Data Scientist in Plymouth, MN. The 6–9 month paid program focuses on data-driven workflows for clinical imaging software, collaborating with AI/ML engineers and software teams to improve data quality, model performance and regulatory compliance.

The role includes hands-on data processing, model training, and an end-of-summer presentation detailing methods, results, and impact within a HealthTech environment.