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You will collaborate closely with our synthesis and characterization team to translate predictions into experiments, improve models with real-world feedback, and build a closed-loop discovery engine. While there is an opportunity to publish selected results, our overarching goal is to deliver new, high-impact materials that can be validated experimentally and ultimately scaled.
This role combines scientific depth with teamwork and execution ownership. You will contribute to strategic decisions on methodology, prioritization, and platform direction. You will receive a competitive compensation package with possible equity participation.
Key responsibilities
Materials discovery & scientific leadership
- Design, implement, and continuously improve DFT-based workflows to predict key functional properties of crystalline materials.
- Develop screening strategies and decision criteria to down-select candidates from large search spaces and guide experimental validation.
- Own the computational plan for assigned discovery programs: define milestones, success criteria, and deliverables; drive execution.
- Build robust, automated, and reproducible DFT pipelines.
- Improve throughput via AI/ML workflow optimization.
- Maintain high-quality documentation, versioned workflows, and traceable datasets.
- Work with synthesis/characterization colleagues to interpret results, troubleshoot discrepancies, and incorporate learnings into improved workflows.
- Communicate results clearly to both computational and experimental teammates (written summaries, internal presentations).
- Where appropriate, contribute to publications, conference abstracts, and grant/partner materials (without compromising IP strategy).
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