Responsibilities
- Design and implement machine learning models that predict organoid development outcomes based on protocol parameters, environmental conditions, and molecular characterization data.
- Develop in silico models that can simulate organoid growth dynamics and identify optimal conditions for reproducible organoid generation.
- Create feedback loops between experimental validation and computational prediction to iteratively improve protocol standardization.
- Collaborate with experimental teams to design validation experiments and with data scientists to integrate multi-modal datasets.
Required Qualifications
- Master's degree or PhD in computer science, engineering, applied mathematics, or a related field with demonstrated experience in AI/ML model development.
- Strong programming skills in Python, R, or similar languages, along with experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Previous experience applying computational methods in biological laboratory settings, with knowledge of experimental design principles and biological data types.
Preferred Qualifications
- Experience with organoid systems or tissue engineering applications.
- Knowledge of differential equations, systems biology modeling, and experience with deep learning approaches for biological applications.
- Familiarity with cloud computing platforms and containerization technologies.
Compensation
Salary Range: $115,000—$130,000 USD