The company is looking to develop new computational tools to reshape drug discovery.
Requirements
- Experience with computational methods for ADME/tox and pK/pD prediction, ideally including knowledge graph methods
- Experience with contemporary AI/ML techniques, including deep learning architectures, and ideally including GNNs
- Extensive hands on experience with high-throughput sequencing data, such as RNA-seq, single-cell RNA-seq data, genomic (whole-genome and exome) data, and/or proteomic data
- Advanced proficiency with Python, R, including for ML (i.e. with PyTorch or TensorFlow) and database management
- Experience with physics-based simulation e.g. for pK/pD modeling
- Experience in long-context sequence modeling
- Direct experience in drug discovery or development
Responsibilities
- Leverage expertise in computational biology and toxicology to predict ADME/tox properties of new drugs
- Help lead the creation of next-generation technology to predict ADME/tox properties of new drugs
- Apply deep learning approaches to predict ADME/tox properties of new drugs
- Drive curation of high-quality datasets
- Write patents, research papers and technical documents
- Participate and present at international conferences
- Work with a cross-functional team of experts to computerize drug discovery
Other
- Ph.D. in computational biology, bioinformatics, computer science, or related data science fields with 4+ years of biopharma industry experience
- Experience leading technical projects
- Excellent communication skills
- Relevant postdoctoral training
- Experience with or knowledge of regulatory drug safety evaluations
