--- title: "Thesis (m/f/d): Learning-Based Control… · Cochstedt · Deine" description: "Cochstedt, Germany · On-site — Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and…" canonical_url: "https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs" meta: "og:description": "Cochstedt, Germany · On-site — Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and…" "og:image": "https://www.dronejobs.app/_og/d/c_JobPosting%2Cslug_deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs%2Cp_Ii9qb2JzL2RlaW5lLXRoZXNpcy1tLWYtZC1sZWFybmluZy1iYXNlZC1jb250cm9sLWFsbG9jYXRpb24tZm9yLW1vcnBoaW5nLXVhdnMi%2Cs_bVn4XKZBUvYanyN8.png" "og:image:height": "630" "og:image:type": image/png "og:image:width": "1200" "og:title": "Thesis (m/f/d): Learning-Based Control… · Cochstedt · Deine" "twitter:image": "https://www.dronejobs.app/_og/d/c_JobPosting%2Cslug_deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs%2Cp_Ii9qb2JzL2RlaW5lLXRoZXNpcy1tLWYtZC1sZWFybmluZy1iYXNlZC1jb250cm9sLWFsbG9jYXRpb24tZm9yLW1vcnBoaW5nLXVhdnMi%2Cs_bVn4XKZBUvYanyN8.png" --- > Index of every page on this site: [llms.txt](https://www.dronejobs.app/llms.txt) [Back to jobs](https://www.dronejobs.app/jobs) ![Logo of Deine](https://assets.jobbb.io/cdn-cgi/image/w=128,h=128,f=auto/logos/01m3vw8wypff0k12c5vazdpfqs_1790863242.png) **Deine** **Actively hiring ** # **Thesis (m/f/d): Learning-Based Control Allocation For Morphing UAVs**
Workplace
**On-site**
Commitment
**Full-time**
Seniority
**Intern**
Posted
18d ago
Location Cochstedt, Germany --- Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and modern flight system technology. From flight dynamics to unmanned aerial vehicles, from simulation to real flight tests - we analyse, test and develop innovations that will shape the flying of the future. **What to expect** This thesis develops a safe, learning-based control-allocation approach for a trailing-edge morphing-wing UAV under model uncertainty. The allocation must meet the primary control objectives while exploiting actuator redundancy for secondary goals. A central part is handling model uncertainty, e.g., via adaptive methods, disturbance observers, set-based methods or statistical learning. Another focus is allocation feasibility, covering backup strategies, slack variables, adaptive weighting, parameter optimization and explicit feasibility certificates. The thesis is jointly supervised by DLR and Prof. Mayank Shekhar Jha (CRAN, CNRS, Université de Lorraine, France). **Your tasks** • Review learning-based methods for safe and feasible control allocation under model uncertainty • Compare uncertainty-handling approaches (adaptive, observer-based, set-based, statistical learning) by their guarantees, assumptions and real-time capability • Develop a learning-based control-allocation concept for a trailing-edge morphing-wing UAV under model uncertainty • Integrate safety mechanisms (e.g., CBF-based safety filters) and strategies to keep the allocation feasible (backup strategies, slack variables, feasibility certificates) • Train and benchmark the developed approaches under varying operating conditions and model uncertainties • Analyze stability, safety and allocation feasibility **Your profile** • Enrolled in a Master’s program in Computer Science, Control Engineering, Robotics, Aerospace Engineering or a related field • Hands-on experience with reinforcement learning is mandatory, ideally with policy-gradient methods such as PPO or SAC • Practical experience with Gymnasium or comparable RL environments is mandatory • Good understanding of control engineering specifically related to flight control design is a plus • Strong Python programming skills • Independent, structured way of working and a strong interest in combining RL, control and safety-critical autonomous systems We look forward to getting to know you! If you have any questions about this position (\*\*Vacancy-ID **6793**) please contact: **Mark Spiller** Tel.: +49 531 295 1159 ```json {"@context":"https://schema.org","@graph":[{"@id":"https://www.dronejobs.app/#website","@type":"WebSite","inLanguage":"en","name":"DroneJobs","url":"https://www.dronejobs.app","publisher":{"@id":"https://www.dronejobs.app/#identity"}},{"@id":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs#webpage","@type":"WebPage","description":"Cochstedt, Germany · On-site — Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and…","name":"Thesis (m/f/d): Learning-Based Control… · Cochstedt · Deine","url":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs","breadcrumb":{"@id":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs#breadcrumb"},"isPartOf":{"@id":"https://www.dronejobs.app/#website"},"potentialAction":[{"@type":"ReadAction","target":["https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs"]}]},{"@id":"https://www.dronejobs.app/#identity","@type":"Organization","description":"Connecting the world's best drone pilots with top-tier companies. The future of flight is here.","name":"DroneJobs","url":"https://www.dronejobs.app"},{"@id":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs#job-posting","@type":"JobPosting","datePosted":"2026-10-28T10:22:08.000000Z","description":"\u003Cp>Welcome to the Institute of Flight Systems. Our work focuses on the interaction between aircraft configuration, pilots and modern flight system technology. From flight dynamics to unmanned aerial vehicles, from simulation to real flight tests - we analyse, test and develop innovations that will shape the flying of the future.\u003C/p>\n\u003Cp>\u003Cstrong>What to expect\u003C/strong>\u003C/p>\n\u003Cp>This thesis develops a safe, learning-based control-allocation approach for a trailing-edge morphing-wing UAV under model uncertainty. The allocation must meet the primary control objectives while exploiting actuator redundancy for secondary goals. A central part is handling model uncertainty, e.g., via adaptive methods, disturbance observers, set-based methods or statistical learning. Another focus is allocation feasibility, covering backup strategies, slack variables, adaptive weighting, parameter optimization and explicit feasibility certificates. The thesis is jointly supervised by DLR and Prof. Mayank Shekhar Jha (CRAN, CNRS, Université de Lorraine, France).\u003C/p>\n\u003Cp>\u003Cstrong>Your tasks\u003C/strong>\u003C/p>\n\u003Cp>• \u00a0 \u00a0Review learning-based methods for safe and feasible control allocation under model uncertainty\u003Cbr />\n• \u00a0 \u00a0Compare uncertainty-handling approaches (adaptive, observer-based, set-based, statistical learning) by their guarantees, assumptions and real-time capability\u003Cbr />\n• \u00a0 \u00a0Develop a learning-based control-allocation concept for a trailing-edge morphing-wing UAV under model uncertainty\u003Cbr />\n• \u00a0 \u00a0Integrate safety mechanisms (e.g., CBF-based safety filters) and strategies to keep the allocation feasible (backup strategies, slack variables, feasibility certificates)\u003Cbr />\n• \u00a0 \u00a0Train and benchmark the developed approaches under varying operating conditions and model uncertainties\u003Cbr />\n• \u00a0 \u00a0Analyze stability, safety and allocation feasibility\u003C/p>\n\u003Cp>\u003Cstrong>Your profile\u003C/strong>\u003C/p>\n\u003Cp>• \u00a0 \u00a0Enrolled in a Master’s program in Computer Science, Control Engineering, Robotics, Aerospace Engineering or a related field\u003Cbr />\n• \u00a0 \u00a0Hands-on experience with reinforcement learning is mandatory, ideally with policy-gradient methods such as PPO or SAC\u003Cbr />\n• \u00a0 \u00a0Practical experience with Gymnasium or comparable RL environments is mandatory\u003Cbr />\n• \u00a0 \u00a0Good understanding of control engineering specifically related to flight control design is a plus\u003Cbr />\n• \u00a0 \u00a0Strong Python programming skills\u003Cbr />\n• \u00a0 \u00a0Independent, structured way of working and a strong interest in combining RL, control and safety-critical autonomous systems\u003C/p>\n\u003Cp>\u00a0We look forward to getting to know you!\u003C/p>\n\u003Cp>If you have any questions about this position (**Vacancy-ID \u003Cstrong>6793\u003C/strong>) please contact:\u003C/p>\n\u003Cp>\u003Cstrong>Mark Spiller\u003C/strong>\u003Cbr />\nTel.: +49 531 295 1159\u003C/p>","directApply":false,"title":"Thesis (m/f/d): Learning-Based Control Allocation For Morphing UAVs","validThrough":"2027-01-26T10:22:08.000Z","employmentType":["FULL_TIME"],"hiringOrganization":{"@type":"Organization","name":"Deine","logo":"https://assets.jobbb.io/logos/01m3vw8wypff0k12c5vazdpfqs_1790863242.png","url":"https://www.dronejobs.app/"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Cochstedt","addressCountry":"DE"}},"mainEntityOfPage":{"@id":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs#webpage"}},{"@id":"https://www.dronejobs.app/jobs/deine-thesis-m-f-d-learning-based-control-allocation-for-morphing-uavs#breadcrumb","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","name":"Home","item":"https://www.dronejobs.app","position":1},{"@type":"ListItem","name":"Jobs","item":"https://www.dronejobs.app/jobs","position":2},{"@type":"ListItem","name":"Thesis (m/f/d): Learning-Based Control Allocation For Morphing UAVs","position":3}]}]} ``` ## Sitemap See the full [sitemap](https://www.dronejobs.app/sitemap.md) for all pages.