Eine maßgeschneiderte Bewerbung für diese Stelle — ein maßgeschneiderter Lebenslauf und ein Anschreiben, die genau zur Stellenanzeige passen.
Constructor Knowledge Labs seeks a PhD-level scientist to build a differentiable quantum simulation and learning engine. You will advance numeric simulation of open and closed systems, integrating Julia and Python interfaces within performance-critical loops.
Responsibilities include parameter inference, model learning from data, and exploring neural-network PDE solvers. You will supervise students and contribute to education materials, while collaborating with Constructor University on research
Location: Remote within Europe, with regular presence in Bremen
Direct Reports: None; supervision of students and interns
Working Model: Remote / hybrid. Regular time on-site in Bremen.
Build the simulation and model-learning engine at the centre of Constructor's quantum software. The work is numerics-heavy: differentiable simulation of physical systems, inference of model parameters from experimental data, and optimisation at scale. The role sits between physics and engineering and requires genuine depth in both.
Success Metrics and KPIs: simulation accuracy against measurement, runtime performance, API stability.
Success Metrics and KPIs: benchmark results, methods adopted by the research group, publications.
Accountability: research collaborations initiated and sustained; students productive.