Internship - Self-supervised learning and pathology foundation models

Barco; West-Vlaanderen; On-site; EUR 8,900 - 13,000; Full time

Job summary

Barco Labs is seeking a Master student for an internship in self-supervised learning and pathology foundation models. You will collaborate with researchers to advance medical imaging and AI-driven pathology workflows.

The role involves exploring foundation models, analyzing tissue content, and evaluating artifact detection methods within digital pathology projects at Barco.

Qualifications

• Master student in Computer Science, AI, Data Science, or related field.
• Strong Python and ML/DL knowledge with hands-on model work.
• Familiarity with PyTorch or TensorFlow and software tools (Linux, Docker, Git).

Responsibilities

• Explore self-supervised learning and foundation models for pathology.
• Analyze tissue content using pre-trained image representations.
• Detect image quality issues with limited datasets and evaluate AI approaches.
• Contribute to improving digital pathology workflows.

Skills

Python

Machine Learning

Deep Learning

PyTorch

TensorFlow

Education

Master's degree in Computer Science or related field

Tools

Linux

Docker

Git

Job description

Internship - Self-supervised learning and pathology foundation models

Master student in Computer Science, AI, Data Science, or related field.

Languages

Required Skills

Python Machine Learning Deep Learning PyTorch TensorFlow Linux Docker Git

Nice to have

Computer Vision Foundation Models Representation Learning Medical Imaging Large-scale AI Training

Soft Skills

Analytical skills Problem-solving Scientific research interest

About

The Position

As an intern at Barco Labs, you'll work on cutting-edge research projects in AI and digital pathology. Collaborate with experienced researchers to improve medical imaging technologies.

The Company

Barco is a global tech company creating AV solutions for professionals. Our mission is to visualize and connect the world, enhancing patient outcomes and decision-making. We value innovation and diversity, driving creativity and collaboration.

Your Responsibilities

• Explore foundation models and self-supervised learning techniques.
• Analyze tissue content using pre-trained image representations.
• Detect image quality issues without extensive datasets.
• Evaluate AI-driven approaches for artifact identification.
• Contribute to improving digital pathology workflows.

Why Join Us?

• Collaborate with experienced researchers
• Hands-on experience with AI technologies