Schrödinger is a science and technology leader with over 30 years of experience developing software solutions for physics-based and machine learning-based chemical simulations and predictive analyses.
We’re seeking a materials science-focused Machine Learning Applications Scientist to join us in our mission to improve human health and quality of life through the development, distribution, and application of advanced computational methods. As a member of our Materials Science team, you’ll have the opportunity to work on diverse projects 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 materials science enthusiast who’s familiar with MatMiner, Dscribe, or other informatics packages
- A proficient Python programmer and debugger who’s familiar with machine learning packages like Scikit-Learn, Pandas, NumPy, SciPy, Keras, PyTorch, and/or TensorFlow
- An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment
What you’ll do:
- Research and analyze data sets using a variety of statistical and machine learning techniques to develop predictive machine learning 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