Responsibilities
- Design and implement state-of-the-art machine learning models (e.g., tree-based algorithms, fingerprint-based methods, multi-layer perceptrons, and graph neural networks) to predict chemical and material properties with unparalleled accuracy.
- Develop and apply generative AI techniques to propose new molecules or materials with desired target properties that push past the limitations of today’s OLEDs.
- Continuously refine and validate predictive models to improve accuracy and reliability ensuring they meet the highest standards for molecular property prediction and compound design.
- Work closely with team members in the computational group to seamlessly integrate AI approaches with traditional chemical research methods such as laboratory experiments, molecular simulations, and quantum chemistry calculations.
- Partner with other R&D scientists to interpret model predictions, guide experiment design, and identify promising leads for new compounds or materials.
Basic qualifications
- PhD in Chemistry, Physics, or a related field (e.g., Chemical Engineering, Materials Science).
- Proven experience applying machine learning to chemical problems, including developing both property prediction (inferential) models and generative models (demonstrated through peer-reviewed publications or successful practical applications).
- Familiarity with a broad range of machine learning approaches relevant to chemistry, such as tree-based models, fingerprint-based similarity methods, multi-layer perceptrons (MLPs), and graph neural network models.
- Proficiency in Python for scientific programming and data analysis, including experience with relevant libraries/frameworks (e.g., PyTorch, RDKit, PyG, etc.).
- Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary team environment.
Preferred qualifications
- Postdoctoral research experience (2+ years) or equivalent advanced research experience applying AI/ML in chemistry.
- Background in organic electronics or related fields (e.g., OLED materials, organic semiconductors) is highly valued.
- Experience with machine learned interatomic potentials (MLIP) including training and implementation is a plus.
- Experience developing user interfaces or graphical tools for scientific software applications (GUI design skills).
- Competitive base salary and annual bonus program
- Medical/Prescription Drug coverage, Dental, and Vision for employees and family
- Option between Flexible Spending account (FSAs) or Health Savings Account (HSA)
- Group Term Life insurance, short term disability, and long-term disability benefits for employees
- Employee Stock Purchase Plan (ESPP)
- 401(k) company contribution
- Ewing Worldwide Headquarters (HQ) cafeteria provides breakfast and lunch to employees at no cost to them
- Annual charitable matching gift
- Generous Paid Time Off
Tags & focus areas
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