# Master Thesis Student (d/f/m) on Physics-aware surrogate modelling and validation at Airbus

Airbus is hiring a Master Thesis Student (d/f/m) on Physics-aware surrogate modelling and validation in Munich, Germany. Level rates it Works on AI ●●●○; you can [apply on Airbus](https://jobsbylevel.com/go/b88e28a2-1586-4149-957c-f7fc701ddd0c?utm_medium=ai-agent).

AI Level 3, AI centrality 75 out of 100. München Area.

## Details

- Company: [Airbus](https://jobsbylevel.com/companies/airbus)
- AI level: AI Level 3 (score 75 out of 100)
- Location: München Area
- Posted: November 12, 2026
- Apply: https://jobsbylevel.com/go/b88e28a2-1586-4149-957c-f7fc701ddd0c?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=master-thesis-student-d-f-m-on-physics-aware-surrogate-modelling-and-validation-at-airbus-c45420

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## Description

Job Description: In order to support E-Aircraft Systems, Airbus Defence and Space is looking for a Master Thesis Student on Physics-aware surrogate modelling and validation (d/f/m) You are looking for a master thesis and want to get to know the work of a job title? Then apply now! We look forward to you supporting us in the Airbus X-Labs department as a Master student (d/f/m)! Location: Taufkirchen / Ottobrunn Start: 01.01.2027 Duration: 6 Months Your location Our site is just a stone's throw away from Munich, the beautiful capital of Bavaria. Are you into sports and other outdoor activities? The Alps and Lake Starnberg are within an hour’s reach, offering a multitude of recreational options. Your benefits Attractive salary and work-life balance with a 35-hour week (flexitime). Traveling overseas or within Germany (team events) is possible after consultation and agreement from the department. International environment with the opportunity to network globally. Work with modern/diversified technologies. Opportunity to participate in the Generation Airbus Community to expand your own network. Thesis detailed description For this thesis, you will join the design and simulation engineering team to work on advanced physics-aware surrogate modeling for megawatt-class cryogenic components, supporting next-generation electric aircraft propulsion research (such as the CryoProp demonstrator). System-level evaluations rely on 0D/1D lumped-parameter physical networks in MATLAB/Simscape; however, standard baseline models often diverge from dynamic test-bench telemetry during transient current steps and gaseous helium (GHe) coolant flow/pressure variations due to complex unmodeled physical interactions (e.g., dynamic contact resistance, thermal inertia, cryostat heat inleak). To overcome the labor-intensive manual parameter tuning and the numerical instability (solver chattering, step-size collapse) of unconstrained black-box machine learning models, your focus will be on developing and validating a physics-aware surrogate model. Furthermore, you will deploy sparse symbolic regression to distill the learned neural dynamics into explicit, human-readable analytical equations that can be integrated back into baseline 1D Simscape model libraries. This thesis is planned to begin in the early 2027 for an ideal duration of 6 months (start date can be adjusted). Your work will directly support the development of robust, virtual integration tools and advance model-based engineering and experimental validation strategies for future electric aircraft propulsion systems. Your tasks and responsibilities Depending on the exact timing and scope of the thesis, the tasks include: Literature review and familiarization with Port-Hamiltonian Systems (PHS), continuous-depth Neural ODEs, symbolic regression frameworks (SINDy, PySR). Analysis of experimental test-rig telemetry to identify unmodeled transient physical dynamics across varying current steps and GHe coolant conditions (T,P,m,IDC). Formulation and training of an AI surrogate using the appropriately identified methods Model compilation and export as an acausal model coupled natively into Simscape to be a part of a larger system model Numerical validation and co-simulation benchmarking to show good performance in a system model Symbolic distillation of the learned dissipation matrix into closed-form, interpretable 1D constitutive equations for physical inspection and loss breakdown. Packaging a reproducible, documented modeling toolset and presenting the technical methodology and simulation results to the engineering team. Desired skills and qualifications You are looking for a Master Thesis in Germany, in Aerospace engineering or similar, and you have the following competences: Enrolled full-time student (d/f/m) in the area of Aerospace engineering or an equivalent field of study. First practical experience in the field of AI surrogate modelling. First experience with handling large experimental

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