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We are seeking a Materials Science Intern to lead machine learning (ML)-driven material discovery for catalytic reactions. This position will focus on developing an ML pipeline interfacing between theoretical and experimental materials science, specifically linking: 3D crystal structure –> synthesis procedure –> characterization data (XRD, XAS, TEM, etc) —> 3D structure. You will work at the interface of ML, computational chemistry, and experimental validation, ensuring of the chemical relevance of the data gathered and the ML models output.
Your role will involve collaborating with the engineering & science team (~20 people) to enhance Entalpic’s materials discovery pipeline, building models which connect simulated discoveries to the real world and providing experimental validation for Entalpic’s candidate materials. You will focus directly on interfacing open-source and AI-generated datasets of 3D structures with synthesis procedure development and characterization data. In so doing, you will work alongside experimental partners and industry clients to ensure that our computational discoveries have a viable path to experimental validation, bridging the gap between AI-driven design and real-world applications.
This position directly supports the company’s mission of discovering materials to optimize carbon intensive industries. You will be responsible for:
Bonus Skills
You will report to the Chief Science Officer (CSO) of Entalpic and will be located in our Paris offices, with up to 2 days of remote work per week.
We are a no-nonsense startup, where we favor a sustainable culture promoting work-life balance and good compensation over football tables and free food. We offer:
Entalpic is dedicated to equal opportunity employment and fosters an environment that is open and respectful of diversity. All applicants are encouraged to apply, even if you don’t meet all above requirements. If you have passion for our mission and believe you can contribute, we want to hear from you.
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