Digital Twin Principal Scientist (f/m/d)
Focused Energy
- Location
- Washington, D.C.
- Employment
- Full Time
- Work model
- On-Site
- Level
- Principal
- Posted
- 1h ago
Skills
About this role
About Focused Energy Focused Energy is building the future of clean energy through laser-driven inertial fusion. Backed by our $240M Series A - the largest fully secured Series A in the global fusion industry - we are scaling rapidly across multiple geographies. Based in Germany and the US, we have brought together the top scientific and engineering minds using proven technologies to unlock fusion power at commercial scale. Focused Energy is working to realize game-changing scientific innovation that promises to deliver a clean, sustainable and abundant source of power globally.
Your Role
Focused Energy is advancing laser-driven inertial fusion energy (IFE) toward commercial power generation, and the credibility and velocity of that mission depend on our ability to model how the full IFE system behaves, where the physics risks lie, and what each subsystem must achieve for the plant to be economically viable. This role exists to and lead and participate in that capability. We are seeking a Principal Scientist to serve as the most senior technical authority on the Digital Twin team to bring together an integrated IFE system modeling environment which will be at the center of Focused Energy’s IFE plant design efforts. You will lead a team of scientists who develop and own the simulation codes for each major subsystem - laser, target, fusion chamber, target injection and tracking, and target manufacturing - and drive the synthesis of those subsystem models into a comprehensive, self-consistent plant-level simulation framework. That framework enables rigorous trade-off analysis across competing point design candidates and allocates quantitative performance budgets back to each subsystem, forming the physics foundation from which engineering requirements are derived.
What You'll Do
Integrate the physics modeling efforts across all IFE subsystems — laser, target, chamber, target injection/tracking, and manufacturing. Validate that subsystem code accurately captures the physics, with model uncertainty rigorously quantified and reported. Set the scientific direction, modeling standards, and validation philosophy for the Digital Twin team. Lead development of a comprehensive, integrated IFE plant model. Work directly with the Science team on trade-off analyses across design candidates, identifying which design choices and parameters most affect plant viability. Work with experimental physics teams to validate simulation codes against real shot data (our own, plus NIF, OMEGA, ELI, etc.). Partner with the Digital Twin team and external collaborators (national labs, the broader IFE community) to keep our modeling at the frontier. Present the modeling team's work to executive leadership, program management, and investors.
Who You Are
Must-haves Master’s degree in Physics (with a specialization in Plasma, Laser, Nuclear, or related field); a Ph.D. is strongly preferred. Deep expertise in IFE/ICF physics - e.g. target implosion design, laser-plasma interaction, hydrodynamic instability, burn physics, or chamber/neutronics Track record developing, validating, and publishing physics simulation codes (hydrodynamic, radiation-hydrodynamics, PIC, ray tracing, Monte Carlo neutronics, etc.) Strong programming skills (Python, Fortran, C++, or equivalent) and experience with HPC/large-scale simulation workflows. Experience with integrated, systems-level modeling - coupling physics domains, managing code interfaces, uncertainty propagation, sensitivity analysis. Nice-to-haves Strong scientific leadership - sets high standards and builds team confidence. Clear communicator - can translate complex physics for execs, engineers, and external partners. Systems-level thinker - sees how subsystem trade-offs affect the whole design. Collaborative across physics, engineering, and software disciplines. Comfortable making decisions under uncertainty and being transparent about confidence levels. Familiarity with Digital Twin methods, reduced-order