Overview
Skills
Job Details
Education: MS, or PhD-level scientist.
Required Skills:
Essential Skills: Strong Programming Foundation: Demonstrated proficiency in Python and its scientific computing ecosystem, including libraries like NumPy, Pandas, Scikit-learn and OpenCV. Version Control: Proficiency with version control systems, particularly Git, and experience with collaborative platforms like GitHub or GitLab.
Computer Vision & Image Analysis: Solid experience in both classical and modern image analysis techniques. This includes traditional image processing and applying machine learning for tasks like image classification and semantic segmentation. Whole-Slide Image (WSI) Handling: Hands-on experience processing and analyzing gigapixel whole-slide images, using libraries such as OpenSlide or similar tools.
Collaborative Mindset: A strong aptitude for iterative design, a proactive approach to receiving and incorporating frequent feedback from cross-disciplinary teams.
Communication Skills: Excellent interpersonal and communication skills, with a proven ability to explain complex computational concepts to pathologists and biologists
Desirable Skills:
Advanced Deep Learning: Deep expertise in developing and implementing advanced deep learning models for digital pathology, including for tasks like instance segmentation. High proficiency with at least one major framework such as PyTorch (experience with object detection libraries like Detectron2 is a plus), TensorFlow, or Keras
High-Performance Computing (HPC): Experience using HPC environments and familiarity with job schedulers, specifically SLURM, for training models on large datasets. Commercial Pathology Software: Practical experience with commercial digital pathology platforms (e.g., HALO, Visiopharm, or QuPath).
Workflow Orchestration: Experience building and managing data pipelines with workflow orchestration tools such as Dagster or Airflow.
Application Development: Experience building simple graphical user interfaces (GUIs) for research tools using Python frameworks like Tkinter or PyQt.
Cloud Computing: Familiarity with cloud computing services for model training and deployment, particularly Amazon Web Services (AWS EC2)