Principal ML Scientist – Predictive Toxicology
Tasks
- Apply federated learning for collaborative development
- Build and optimize molecular AI models
- Collaborate with customers and scientific partners
- Define modeling approaches and biological endpoints
- Design data strategies for safety and efficacy
- Develop predictive toxicology models
- Develop quantitative biology models
- Integrate high throughput screening workflows
- Integrate image based screening workflows
- Integrate multi-omics workflows
- Lead predictive toxicology scientific strategy
- Mentor scientists and build capabilities
- Support compound prioritization
- Translate scientific challenges into AI solutions
Perks/Benefits
- Co-working stipend
- European Office Days
- Generous holiday allowance
- Learning and professional development budget
- Mental health support
- Remote-first flexibility
- Wellbeing budget
- Work from home budget
Skills/Tech-stack
Cheminformatics | Compound Prioritization | Computational Biology | Data strategy | Deep learning | Federated Learning | Graph Neural Networks | High Throughput | High Throughput Screening | Image Based Screening | Language Processing | Machine Learning | Model Evaluation | Model Optimization | Molecular Modeling | Multi-Omics | Natural Language | Natural Language Processing | Neural Networks | Privacy-preserving | Privacy-preserving machine learning | RNA-seq | Transformer
Education
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