Deciphering the mechanisms of life and advancing rational drug design by developing next-generation, structure-centric, multimodal foundation models
Requirements
- PhD in Computer Science, Machine Learning, Computational Biology, or a related technical field
- Strong research background in deep learning, with a publication record in areas such as NLP, computer vision, generative modeling, reinforcement learning, or biological applications
- Proficiency in Python and ML frameworks such as PyTorch
- Proven ability to independently drive research ideas from concept to working prototype
- Deep intellectual curiosity and a proactive approach to engaging with unfamiliar scientific problems and disciplines
Responsibilities
- Collaborate closely with a multidisciplinary team of ML researchers, computational biologists, and chemists to tackle cutting-edge scientific challenges in molecular modeling
- Develop and optimize large-scale models that integrate diverse biological modalities (e.g., sequences, 3D structures, molecular properties) to address complex biomolecular challenges—ranging from conformational landscape modeling to interaction and binding affinity prediction, and design of proteins and small molecules
- Stay current with advances in machine learning and proactively integrate cutting-edge techniques into our biomolecular models
Other
- PhD in Computer Science, Machine Learning, Computational Biology, or a related technical field
- Strong research background in deep learning
- Ability to work in a collaborative environment
- Ability to interact and occasionally have unsupervised contact with internal/external clients and/or colleagues
- Ability to appropriately handle and manage confidential information
