Image Analysis​/Machine Learning Scientist

Image Analysis / Machine Learning Scientist

The Image Analysis / Machine Learning Scientist will be responsible for the design, development, optimization, validation, and deployment of advanced machine learning and computer vision solutions that support computational pathology, biomarker discovery, and translational research initiatives. This role combines expertise in artificial intelligence, digital pathology, image analytics, and data science to generate quantitative insights from complex histological and biomedical imaging datasets.

The successful candidate will develop innovative image analysis algorithms, deep learning models, and automated workflows to support tissue characterization, biomarker quantification, cellular phenotyping, and predictive modeling. Working closely with computational pathologists, pathologists, translational scientists, bioinformaticians, and software engineers, this individual will help drive the adoption of AI‑enabled methodologies that improve scientific decision‑making and accelerate research outcomes. The role requires hands‑on experience applying machine learning, deep learning, and computer vision techniques to large‑scale biomedical image datasets, including whole‑slide pathology images, microscopy images, and multiplex imaging platforms.

The candidate must be capable of translating scientific objectives into robust analytical solutions while ensuring accuracy, reproducibility, and scalability.

• Design, develop, train, and deploy machine learning and deep learning models for computational pathology and biomedical imaging applications.
• Develop supervised, unsupervised, and semi‑supervised learning approaches for image classification, segmentation, object detection, and feature extraction.
• Optimize model performance through hyperparameter tuning, architecture selection, and performance evaluation.
• Implement explainable AI methodologies to support scientific interpretation and regulatory transparency.
• Evaluate emerging AI technologies and assess applicability to computational pathology challenges.
• Develop scalable machine learning workflows capable of supporting high‑throughput image analysis environments.

• Design and implement advanced image analysis pipelines for histopathology and digital pathology applications.
• Develop algorithms for tissue segmentation, cellular detection, phenotyping, spatial analysis, and morphological characterization.
• Apply computer vision techniques to extract biologically relevant features from histological and microscopy images.
• Develop automated workflows to process whole‑slide images and large imaging datasets.
• Improve image quality, artifact detection, normalization, and preprocessing methodologies.
• Support quantitative analysis of tissue biomarkers and disease‑related features.

• Collaborate with computational pathologists to develop AI‑driven pathology solutions.
• Support development and optimization of digital pathology workflows for biomarker discovery and translational research.
• Analyze whole‑slide imaging data using commercial and open‑source pathology platforms.
• Develop image analysis approaches for:
  • Immunohistochemistry (IHC)
  • Immunofluorescence (IF)
  • Multiplex imaging
  • Spatial biology applications
  • Tissue morphology analysis
• Contribute to validation and deployment of computational pathology algorithms.

• Support identification and validation of image‑derived biomarkers.
• Develop analytical methods to quantify biomarker expression and spatial relationships within tissues.
• Collaborate with translational scientists to integrate image‑derived data with molecular and clinical datasets.
• Generate quantitative insights that support preclinical and clinical research programs.
• Assist in developing predictive models associated with disease progression, treatment response, and patient stratification.

• Design and execute validation studies for machine learning models and image analysis workflows.
• Evaluate model accuracy, precision, sensitivity,…