Machine Learning-Enhanced DFT Scientist
As a member of our Materials Science team, you’ll work closely with our Machine Learning and Applications Science teams to develop new hybrid simulation frameworks combining ML and density functional theory (DFT) to provide significant advantage in terms of efficiency and/or accuracy for critical property prediction impacting applications in optoelectronics, catalysis, energy storage, semiconductors, aerospace, and specialty chemicals.
Who will love this job:
- A statistical and machine learning expert with robust problem-solving skills
- A scientist with deep knowledge of DFT and other electronic structure methods who understands the limitations and appropriate applications of these methods
- A materials science enthusiast who’s familiar with MatMiner, Dscribe, or other informatics packages
- A proficient Python programmer who knows machine learning packages like Scikit-Learn, NumPy, SciPy, Pandas, and PyTorch
- An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment
What you’ll do:
- Research and analyze large data sets generated using quantum chemical methods at scale to develop predictive machine learning models, both for direct training and feature generation for calibration models
- Communicate results and present ideas to the team
- Develop tools and workflows that can be integrated into commercial software products
- Work with customers on various machine learning-centric Materials Science research projects
- Validate existing Schrödinger machine learning products using public data sets or internally generated data sets
What you should have:
- A PhD (or extensive experience) in Chemistry, Materials Science, Engineering, Computer Science, or Physics
- Hands-on experience with the application of machine learning, neural networks, deep learning, data analysis, or chemical informatics to materials and complex chemicals
Schrödinger builds advanced chemical simulation software designed for pharmaceutical, biotechnology, and materials research. Targeting scientists and researchers, their platform streamlines the discovery of novel molecules, enabling faster and more cost-effective drug development and material innovation.
- Founded
- Founded 1990
- Employees
- 500+ employees
- Industry
- Life Sciences Tools & Services
- Total raised
- $160M raised