Deciphering the mechanisms of life and advancing drug design by developing next-generation, structure-centric, multimodal foundation models
Requirements
- Extensive experience with computational tools and workflows in structural or computational biology, including molecular dynamics (e.g., GROMACS, AMBER), docking (e.g., AutoDock, Glide), quantum chemistry (e.g., ORCA, Gaussian), structure prediction and design (e.g., Rosetta, RFDiffusion), or analysis of cryo-EM/X-ray data
- Hands-on experience integrating wet-lab and computational data, and applying structural or biological insights to address complex biological problems
- Proficiency in programming and scientific computing (e.g., Python, C++, bioinformatics pipelines)
Responsibilities
- Collaborate closely with a multidisciplinary team of ML researchers, computational biologists, and chemists to tackle cutting-edge scientific challenges in molecular modeling
- Contribute to the design, training, and optimization of large-scale models for structure prediction across diverse biomolecular systems, addressing key challenges such as conformational sampling, binding affinity estimation, and de novo molecular generation
- Translate insights from structural biology, experimental data, and physical principles into scalable model architectures and generative algorithms
Other
- Currently pursuing a PhD in computational biology, structural biology, or computer-aided drug design (CADD)
- Strong scientific curiosity, a collaborative mindset, and the ability to quickly learn new concepts and tools
- Ability to interact and occasionally have unsupervised contact with internal/external clients and/or colleagues
- Ability to appropriately handle and manage confidential information including proprietary and trade secret information and access to information technology systems
- Ability to exercise sound judgment
