Postdoctoral Scientist, Molecular Informatics
- Cambridge, MA
- Department: Research & Development
- Region: US
The Molecular Informatics team at Biogen is searching for a postdoctoral researcher interested in machine learning applications and transformative artificial intelligence algorithms to accelerate and improve the established small molecule design cycle that consists of idea generation, evaluation, and synthesis. This is a unique opportunity to join a cross-functional team working at the intersection of computational, chemical, and biological sciences and have direct access to a wealth of structured and unstructured data across the Biogen research organization to make a massive impact on redefining the early drug-discovery platform.
The post-doctoral fellowship focus will be in the implementation and validation of novel deep reinforcement learning architectures in the field of computational chemistry and cheminformatics to improve cycle time optimization in medicinal chemistry and achieve drug candidate selection with both greater speed and superior molecules. Detailed description of role including but not limited to:
- Development of a fully integrated molecular design generation platform by applying deep reinforcement learning and other artificial intelligence algorithms in combinations with Open-Source cheminformatics toolkits.
- Evaluate and apply current state-of-the-art transferable machine learning potentials for small molecules conformational energies based on deep learning algorithms.
- Collaborate closely with the Computational Chemistry group and other cross-functional Drug Discovery teams to apply novel algorithms to relevant therapeutic projects.
- Publish original work in top peer-reviewed journals as well as present his/her research work at internal and external meetings.
- Recent PhD (0-3 years) in cheminformatics, computer science, or relevant scientific fields with thesis focus in machine learning.
- Advanced knowledge of the Python programming language and related open-source libraries.
- Machine learning experience building classical statistical models (e.g. Random Forests, SVM, Light GBM, XGBOOST) and basic statistics.
- Strong publications record in fields related to machine learning and artificial intelligence applied to drug discovery.
- Working experience with modern deep generative models for molecule generations, such as VAE, RNN, LSTM, and GAN.
- Experience with multi-objective optimization algorithms, and their application to deep reinforcement learning architectures.
- Experience building CNNs, RNNs, and other neural network infrastructure from scratch using deep learning libraries (e.g. TensorFlow, Keras, PyTorch).
This Postdoctoral Scientist position is for a fixed duration of three years (and extendable by an additional year). Applications will be accepted until the position is filled. Funding is available for the selected applicant to start immediately. When electronically applying for this position, please include (in one PDF document) a cover letter, curriculum vitae, publication list and a brief description of research interests.
For more information click the link: https://jobs.smartrecruiters.com/Biogen/743999713509109-postdoctoral-scientist-molecular-informatics.