Materials AI Scientist & Partner Project Lead

US Health Partners, LLC

Greater London

Hybride

GBP 70 000 - 90 000

Plein temps

Il y a 3 jours
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Avantages offerts par ce poste

Equity in the company
Holiday entitlement (28 days / 21 days
Parental leave

Résumé du poste

CuspAI is seeking a highly motivated scientist to drive partner projects from scoping to delivery, bridging experimental and computational teams. You will design campaigns, apply simulation and ML tools, and translate platform insights into actionable experimental plans.

Based in London, Cambridge, Amsterdam, or Berlin, you will collaborate with top researchers to advance materials science and AI-enabled discovery, while contributing to scalable workflows and data pipelines.

Qualifications

  • PhD in chemistry, materials science, physics, chemical engineering, or related field.
  • Deep hands-on expertise in atomistic simulation or experimental materials/chemistry with automation or ML/optimisation.
  • Proven ability to communicate across experimental and computational teams.

Responsabilités

  • Lead partner projects from scoping to delivery with technical programme managers.
  • Design and steer discovery campaigns and agentic/closed-loop campaigns with partner labs.
  • Translate platform recommendations into executable experimental plans and ensure data quality.

Connaissances

PhD in chemistry or related field
Atomistic simulation expert
Python for data handling
Strong communication across teams

Formation

PhD in chemistry, materials science, physics, chemical engineering, or equivalent

Description du poste

CuspAI is seeking a highly motivated scientist to drive partner projects from scoping to delivery, bridging experimental and computational teams. You will design campaigns, apply simulation and ML tools, and translate platform insights into actionable experimental plans.

Based in London, Cambridge, Amsterdam, or Berlin, you will collaborate with top researchers to advance materials science and AI-enabled discovery, while contributing to scalable workflows and data pipelines.

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