Principal ML Scientist – Predictive Toxicology
Tasks
- Apply federated learning for privacy preserving model development
- Build and optimize molecular AI models
- Collaborate with customers on model evaluation adoption and roadmap
- Define modelling approaches data strategies
- Develop predictive toxicity and quantitative biology models
- Integrate multiomics image screening and high throughput screening workflows
- Lead predictive toxicology scientific strategy
- Mentor scientists and build scientific capabilities
- Translate scientific challenges into practical AI solutions
Perks/Benefits
- Co-working stipend
- Generous holiday allowance
- Learning and professional development budget
- Mental health support
- Remote-first
- Wellbeing budget
- Work from home budget
Skills/Tech-stack
Cheminformatics | Compound Prioritization | Computational Biology | Cytotoxicity Modeling | DILI Assessment | Data Privacy | Deep learning | Distributed Computing | Federated Learning | Genomics | Genotoxicity Modeling | Graph Neural Networks | High Throughput | High Throughput Screening | Image Based Screening | Language Processing | Machine Learning | Molecular AI | Multiomics | Natural Language | Natural Language Processing | Neural Networks | Quantitative biology | RNA Sequencing | Transformer Models
Education
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