Description

You will tackle complex biological questions by developing state-of-the-art machine learning methods and multimodal integration models to advance organoid engineering and high-throughput screening.

Requirements

• •Master’s degree, ongoing masters, or PhD studies in related technical field
• •Proficiency in Python, ML frameworks, and modern software engineering tools
• •Excellent communication and data visualization skills
• •Drive to tackle biomedical problems and enthusiasm for ML applications
• •Experience applying ML to biomedical data or open-source contributions (Nice-to-have)

Tasks

• •Develop ML methods for organoid phenotyping
• •Design high-throughput screening techniques
• •Analyze rich, high-content datasets
• •Apply state-of-the-art machine learning methods
• •Create predictive models for perturbation screens
• •Integrate multimodal imaging and genomics data
• •Build foundational models for organoid phenotyping
• •Develop predictive methods for cell fate engineering
• •Discover causal mechanisms from perturbation experiments
• •Implement active learning for iterative experimental design

Tools & Technologies

Benefits