Roche
PhD Student - Machine Learning for Biosystems Engineering(m/w/x)
Full-timeOn-siteExperienced
Basel
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Developing machine learning methods for organoid phenotyping and high-throughput screening at biotech firm focused on healthcare access. Ongoing Master's or PhD studies in a technical field required. Expert mentorship provided.
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
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- JAX
- PyTorch
- Git
- CI/CD
- scRNA/ATAC-seq
Benefits
Informal Culture
- Personal expression culture
- Open dialogue environment
Other Benefits
- Scientific community membership
Mentorship & Coaching
- Expert mentorship
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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Developing machine learning methods for organoid phenotyping and high-throughput screening at biotech firm focused on healthcare access. Ongoing Master's or PhD studies in a technical field required. Expert mentorship provided.
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
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- JAX
- PyTorch
- Git
- CI/CD
- scRNA/ATAC-seq
Benefits
Informal Culture
- Personal expression culture
- Open dialogue environment
Other Benefits
- Scientific community membership
Mentorship & Coaching
- Expert mentorship
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Roche and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Roche
Industry
Pharmaceuticals
Description
The company is dedicated to advancing science and ensuring access to healthcare for everyone.
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