BACKGROUND

RESPONSIBILITIES

• Develop and apply protein engineering and design algorithms with minimal supervision
• Develop, validate and document software that implements protein simulation and optimization methods
• Work closely with the software engineering team to meet software quality requirements
• Collaborate with protein engineering and therapeutic development teams to effectively apply protein engineering and analysis software for lead optimization
• Peer-review and mentor junior members in the team
• Participate in the preparation of patents and publications

REQUIREMENTS

• A Ph.D. in Physics, Chemistry, Applied Math, Computer Science or a related field
• A minimum of three years of industrial or relevant post-doctoral experience developing and implementing molecular modeling algorithms. Industrial experience is strongly preferred. Areas of interest include, but are not limited to: Molecular dynamics/Monte Carlo simulations, Force field/potential development, Protein structure optimization, Free Energy Simulations, Antibody modeling
• Demonstrated aptitude for developing practical and novel applications of molecular simulations methods for addressing therapeutic protein optimization
• Clearly demonstrated capacity to independently lead development efforts as well as guide activities of others
• Proficient in Python, or other programming languages (C/C++, Fortran)
• Fluent in written and spoken English

PREFERENCES

• Experience optimizing numerically intensive code
• Protein bioinformatics experience, including but not limited to: Prediction of protein properties from sequence data, Extraction of residue co-evolution data from sequence data, Binding target prediction/identification from sequence data
• Experience implementing and applying machine learning methods to protein modeling problems. Methods of interest include, but are not limited to: clustering methods and other unsupervised learning techniques (ie SOMs, kernel PCA, ICA), regression methods (ie random forest regression, Gaussian process regression), classification methods (ie random forests, SVMs, logistic regression)

COMPENSATION

HOW TO APPLY