Deep Learning Intern: Ophthalmic Imaging Research

Study Association CognAC (SV CognAC); Netherlands; Hybrid; EUR 9,000 - 13,000; Full time

Job summary

Icare Finland Oy, part of Revenio Group, invites a Deep Learning Research Intern to collaborate with our team on AI models for ophthalmic image analysis. You will explore foundational models, self-supervised learning, and real-world radiology-type tasks in a medical imaging context.

The role involves working with deep learning engineers to build scalable experiments, using Kubeflow for reproducible pipelines, and evaluating various architectures.

Qualifications

• Currently pursuing or recently completed a degree in a relevant field with a focus on deep learning, machine learning, or computer vision.
• Proficient in Python with experience in deep learning frameworks (preferably TensorFlow).
• Solid understanding of deep learning concepts and evaluating model performance.
• Ability to critically analyse research papers and communicate findings clearly.

Responsibilities

• Conduct literature research to assess state-of-the-art practices.
• Work with large-scale ophthalmic imaging datasets, implementing self-supervised learning algorithms.
• Evaluate the impact of training paradigms, data, and model architectures on downstream tasks.
• Collaborate with other DL and DevOps engineers to set up pipelines using Kubeflow for reproducible experiments.

Skills

Python

TensorFlow

Deep learning

Research papers analysis

Communication

Education

Bachelor's or Master's in Computer Science, Electrical Engineering, Biomedical Engineering

Tools

Kubeflow

Job description

Icare Finland Oy, part of Revenio Group, invites a Deep Learning Research Intern to collaborate with our team on AI models for ophthalmic image analysis. You will explore foundational models, self-supervised learning, and real-world radiology-type tasks in a medical imaging context.

The role involves working with deep learning engineers to build scalable experiments, using Kubeflow for reproducible pipelines, and evaluating various architectures.