We’re seeking candidates to join us in our mission to accelerate computational methods and discovery in materials and chemistry. 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). The developed tool will be used for discovering high throughput simulations of catalysis, energy materials, and chemicals. 

You will be a member of a multi-disciplinary team working in a fast-paced startup environment. If you are motivated on building revolutionary models and enjoy working on ambitious projects QpiVolta is the right place for you. 

Qualifications and Experience

• Experience in Computational Chemistry, Materials Science, Engineering, or Physics

• Deep knowledge of DFT and other electronic structure methods who understands the limitations and appropriate applications of these methods
• Statistical and machine learning expertise with robust problem-solving skills
• Familiarity with ASE, PyMatgen, RdKit MatMiner or other informatics packages
• Proficiency in  Python  and can use machine learning packages like Scikit-Learn, NumPy, SciPy, Pandas, and PyTorch
• Motivation, Dedication and enjoys collaborating with an interdisciplinary team in a fast-paced environment

• Communicate results and present ideas to the team

• Hands-on experience with the application of machine learning, neural networks, deep learning, data analysis, or chemical informatics to materials and complex chemicals is a plus

  If you are interested send your resumes at -  [email protected]