Battery Machine Learning Force Fields Scientist
New York
Full Time USD 120K - 145K
Schrödinger
Schrödinger is the scientific leader in developing state-of-the-art chemical simulation software for use in pharmaceutical, biotechnology, and materials research.Schrödinger seeks a Battery Machine Learning Force Fields Scientist to aid 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 Machine Learning team, you’ll play a key role in developing a next generation simulation platform for battery materials and testing the limits of classical approximations to quantum mechanics. You’ll also have the opportunity to communicate and collaborate with team members across many different departments and disciplines. Who will love this job:- An innovator who wants to contribute to leading battery chemistry simulation models
- An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment
- A computational scientist who wants to push the limits of what can be simulated at the atomic level
- An excellent communicator and documenter who has experience collaborating teams of diverse scientists
What you’ll do:
- Develop new atomistic machine learning force fields with improved accuracy and generalization
- Contribute to a platform to simulate chemical processes at the electrode/electrolyte interface and discover new battery materials
- Generate and train on sets of millions of quantum chemistry simulations
- Apply best practices from the world of software engineering
- Build scientific validation tests to ensure models continue to perform well
What you’ll need:
- PhD in Chemistry, Materials Science, Chemical Engineering, Physics, or a related field OR a scientific degree plus two to three years' relevant industry experience
- Background in applying molecular simulation and quantum chemistry methods
We’d prefer to hire someone with:
- Experience developing interatomic potentials based on machine learning or classical force fields
- Expertise in the simulation of electrode and electrolyte materials, interfaces, and ion transport
- Familiarity with enhanced sampling, rare-event sampling, or transition state search methods
- Proficiency with Python libraries such as NumPy, SciPy, and PyTorch
Tags: Chemistry Engineering Machine Learning NumPy PhD Physics Python PyTorch SciPy Testing
Perks/benefits: Career development Competitive pay Equity Flex hours Flex vacation Health care Lunch / meals Parental leave Startup environment Team events
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