Computational Biologist

We are seeking a highly skilled Machine Learning Engineer to join our team in developing and optimizing deep learning models for histopathology image analysis.

This role is ideal for researchers who aim to apply advanced computational methods to biomedical challenges.

• Design, develop, and train convolutional neural networks (CNNs) and related ML models on H&E-stained histology images.
• Use and extend tools such as QuPath for cell annotations, segmentation models, and dataset curation.
• Preprocess, annotate, and manage large image datasets to support model training and validation.
• Collaborate with cross-disciplinary teams to integrate image-based predictions with molecular and clinical data.
• Analyze model performance and contribute to improving accuracy, efficiency, and robustness.
• Document research findings and contribute to publications in peer-reviewed journals.

We require the following qualifications:

• PhD in Computer Science, Biomedical Engineering, Data Science, Computational Biology, or a related discipline.
• Demonstrated research experience in machine learning, deep learning, or biomedical image analysis (e.g., publications, thesis projects, or conference presentations).
• Strong programming skills in Python and experience with ML frameworks such as TensorFlow or PyTorch.
• Familiarity with digital pathology workflows, image preprocessing/augmentation, and annotation tools.
• Ability to work collaboratively in a multidisciplinary research environment.

The following qualifications are preferred:

• Background in cancer histopathology or biomedical image analysis.
• Knowledge of multimodal data integration, including spatial transcriptomics.