Description

You will bridge the gap between computational innovation and experimental biology by developing cutting-edge machine learning methods to solve complex challenges in organoid engineering and drug discovery.

Requirements

• •Master’s student or recent graduate in computational biology, computer science, machine learning, bioinformatics, or related technical field
• •Proficiency in Python and modern ML frameworks
• •Knowledge of modern software engineering tools and methodologies
• •Strong fundamentals in linear algebra, statistics, and genomics ML
• •Excellent communication skills in English
• •Skill in data visualization and communicating complex findings
• •Drive to tackle biomedical problems and translate ML methods
• •Track record of relevant publications or open-source contributions
• •Experience applying ML methods to biomedical data
• •Experience in single-cell genomics data analysis and computer vision
• •Experience working with experimental collaborators

Tasks

• •Lead a research project on organoid engineering
• •Develop computational methods for drug discovery
• •Apply machine learning to human biology questions
• •Analyze high-content datasets from perturbation experiments
• •Collaborate with experimental scientists to shape research
• •Create predictive ML methods for high-throughput screens
• •Integrate multimodal imaging and genomics data
• •Build foundational models for organoid phenotyping
• •Develop predictive methods for cell fate engineering
• •Discover causal mechanisms from high-content experiments
• •Implement active learning for iterative experimental design
• •Publish research findings in academic journals
• •Contribute to open-source computational tools
• •Advance next-generation human model systems

Tools & Technologies

Benefits