Job
- Level
- Erfahren
- Ort
- Darmstadt
- Arbeitsmodell
- Onsite
- Job Feld
- Software, Data
- Anstellung
- Vollzeit
- Vertragsart
- Unbefristetes Dienstverhältnis
Job Zusammenfassung
In dieser Position entwickelst du Machine Learning Modelle zur Optimierung und Beschleunigung von multiphysikalischen Simulationen in Digital Twin Umgebungen, indem du eng mit Physikern und Ingenieuren zusammenarbeitest.
Job Technologien
Deine Rolle im Team
- This role sits at the intersection of ML engineering and computational physics, working within our Software Engineering & Digital Twin environments.
- You'll collaborate closely with optical engineers, laser and fusion physicists, simulation scientists, and systems engineers to embed data-driven intelligence directly into our Digital Twin architecture.
- You'll be reducing design risk, shortening development timelines, and unlocking system-level optimization that purely physics-based approaches can't achieve at the speed we need.
- You will design, build, and deploy machine learning models that enhance, accelerate, and augment our multiphysics simulation environments across our high-energy laser systems, target injection and tracking system, target manufacturing system, and fusion chamber.
- This will enable faster design cycles, predictive system optimization, and real-time decision support across our laser, targetry, and fusion programs.
- Surrogate & reduced-order modeling: Design and deploy surrogate and reduced-order models (ROMs) that replace or accelerate high-fidelity multiphysics simulations in the Digital Twin environment.
- Physics-informed ML: Develop physics-informed machine learning (PIML) and physics-informed neural networks (PINNs) that embed physical constraints (Maxwell's equations, thermodynamics, fluid dynamics) directly into model architectures.
- ML pipelines: Build and maintain ML pipelines for training, validation, uncertainty quantification (UQ), and continuous model refinement against experimental and simulation data.
- Active learning & Bayesian optimization: Implement active learning and Bayesian optimization workflows to intelligently guide design space exploration and reduce costly simulation runs.
- Digital Twin integration: Integrate trained ML models into the broader Digital Twin framework, interfacing with HPC simulation outputs (COMSOL, ANSYS, custom solvers) and real-time sensor data.
- Anomaly detection: Develop anomaly detection and predictive diagnostics models to monitor system health and identify off-nominal behavior in laser subsystems.
- Autonomous optimization: Apply reinforcement learning and Bayesian control approaches to support autonomous or semi-autonomous optimization of laser operating parameters.
- Cross-functional collaboration: Collaborate with Digital Twin architects, systems engineers, and optical simulation scientists to ensure ML models meet fidelity, latency, and uncertainty requirements.
- Engineering rigor: Establish best practices for model versioning, reproducibility, testing, and documentation in a fast-moving research environment.
Unsere Erwartungen an dich
Qualifikationen
- Must-Haves
- Master's or PhD in Machine Learning, Computational Physics, Applied Mathematics, Data Science, Computer Science, or a closely related field.
- Fluency working with PDE-based simulation outputs, time-series sensor data, and high-dimensional parameter spaces typical of multiphysics environments.
- Ability to communicate model behavior, confidence intervals, and limitations clearly to physicists, engineers, and non-ML specialists.
- Strong cross-functional team skills; comfort working in an interdisciplinary environment spanning physics, engineering, and software.
- Nice-to-Haves
- Background in laser physics, plasma physics, high-energy-density science, or related complex physics domains.
- Familiarity with multidisciplinary design optimization (MDO) workflows, including Design of Experiments (DoE), sensitivity analysis, and uncertainty propagation.
- Familiarity with Monte Carlo methods and statistical uncertainty quantification frameworks.
- Interest in fusion energy, advanced laser systems, or high-energy-density physics.
Erfahrung
- Proven experience building, training, and deploying ML models for complex physical systems; strong command of deep learning (PyTorch, TensorFlow/JAX), probabilistic models, and uncertainty quantification.
- Expert-level Python; proficiency in C++ or Fortran a plus; experience with HPC environments (batch schedulers, MPI/OpenMP parallelization).
- Hands-on experience with Gaussian processes, neural network surrogates, reduced-order models, or equivalent metamodeling techniques.
- Experience building robust ML pipelines for scientific data - including preprocessing, feature engineering, model validation, and deployment.
- Experience with physics-informed neural networks (PINNs) or neural operators (DeepONet, FNO) applied to physical systems.
- Experience with digital twin platforms and live integration of ML models with simulation environments (e.g., NVIDIA Omniverse, Siemens Xcelerator, ANSYS Twin Builder).
- Experience applying reinforcement learning to physical system control or optimization.
- Experience with MLOps tooling (MLflow, Weights & Biases, DVC) in a scientific computing context.
Unser Angebot
- Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience.
- Certain roles may be eligible for incentive compensation, equity, benefits.
Themen mit denen du dich im Job beschäftigst
Job Standorte
Das ist dein Arbeitgeber
Focused Energy
Die Focused Energy GmbH ist ein deutsch-amerikanisches Unternehmen mit Büros in Darmstadt und Berlin, das sich auf die Entwicklung von Technologien für lasergetriebene Fusion konzentriert. Das Unternehmen hat sich zum Ziel gesetzt, Fusionsenergie im industriellen Maßstab verfügbar zu machen und vereint wissenschaftliche sowie technische Expertise aus Deutschland und den USA.
Description
- Unternehmenstyp
- Startup
- Arbeitsmodell
- Onsite
- Branche
- Energiewirtschaft, Umwelt
Machine Learning Engineer
- Ort
- Darmstadt
- Arbeitsmodell
- Onsite
- Diversität
- Für alle Personen geeignet (m/w/d)
- Nur Englisch
- Nur Englisch erforderlich
