We're seeking a research scientist to develop and optimize ML methods that bridge simulation, protein modeling, and small molecule design. This role requires both strong machine learning foundations and the ability to work at the interface of computational biology, structural modeling, and chemistry.
What you will do- Develop generative models for protein conformational ensembles trained on simulation and experimental data
- Build novel ML architectures for ensemble-based affinity prediction and generative chemistry
- Adapt methods from research literature to disordered protein targets
- Design scalable training pipelines for high-throughput experimentation
- Collaborate with computational and experimental scientists to validate models against biophysical data
What we are looking for- Strong foundation in machine learning theory and implementation
- Deep understanding of modern generative models with application to the sciences
- Background in computational sciences involving biology and chemistry
Preferred- MS or PhD in machine learning with application to computational science
- Strong publication record
- Experience with molecular simulation and protein modeling
