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Research Assistant
Biomedical Image Analysis Scientist Full-time NEW
About the Role
We are looking for a motivated Biomedical Image Analysis Scientist to join our interdisciplinary team of AI researchers and computational scientists. You will develop and apply advanced image processing and AI/ML techniques to analyze biomedical, histopathology, and microscopy data to support scientific discovery and therapeutic development.
Key Responsibilities
Design, implement, and optimize algorithms for biomedical image analysis using deep learning and computer vision techniques (CNNs, transformers, diffusion models, etc.).
Work on multi-modal datasets — including cellular images, microscopy, MRI, CT, EEG, ECG and histopathology slides — to extract meaningful biological or clinical insights.
Develop automated image & waveform segmentation, classification, and feature extraction workflows.
Collaborate with biologists, chemists, and AI engineers to integrate imaging data with omics and molecular datasets.
Contribute to building scalable pipelines for image data preprocessing, model training, and deployment.
Validate models against experimental data and ensure reproducibility of results.
Stay current with advances in biomedical imaging, AI/ML architectures, and data integration methods.
Required Skills & Experience
Strong background in Biomedical Engineering, Computer Science, Electrical Engineering, Biophysics, Computational Biology, or related field.
PhD or Master’s degree with experience in biomedical image analysis or computer vision.
Hands-on experience with Python, PyTorch (or TensorFlow), OpenCV, and scikit-image.
Experience with microscopy, histopathology, and radiology image datasets.
Knowledge of image preprocessing, segmentation, and 3D/volumetric data handling.
Familiarity with data annotation, weak supervision, or multi-modal learning is a plus.
Excellent communication and problem-solving skills.
Preferred Qualifications
Prior experience integrating image data with omics, clinical, or molecular datasets.
Experience deploying ML models or using MLOps tools (e.g., MLFlow, ClearML).
Publications or open-source contributions in biomedical imaging or computational biology