Lead Flight Dynamics & Controls Engineer - blueflite - Brighton
Are you interested in the aerospace sector and working in a crazy cool, rapid-paced startup where your input and decisions matter? Yes? Read on!
blueflite designs and manufactures high performance, electric, unmanned, aerial tilt-rotor vehicles for the delivery of light cargo. Our UAV platform is scalable and has unique and patent-pending design characteristics resulting in advanced capability, regarding maneuverability and vehicle control. blueflite has experience in carbon fiber structure design and manufacturing, design of propulsion systems, developing advanced flight control algorithm, implementation of code in on-board computer systems, and developing a digital ground infrastructure.
We are looking for innovative and solutions-oriented engineers and entrepreneurs to be part of our amazing technology development journey, creating the next generation of logistic technology platforms centered around our unique, agile, and robust UAV design.
blueflite has a truly global outlook to become the No. 1 player in the global cargo UAV industry. We embrace and promote thinking outside the box, an agile approach (without the bureaucracy), and a real can-do attitude. We have high expectations on the quality of work we do and set ourselves high standards without cutting any corners. Daily collaboration with small teams all working toward a world class UAV technology platform is core to our business.
About the Role
The blueflite team is developing a new tool which uses machine learning and reasoning techniques to support autonomous operations of vertical lift systems. To further expand the control capabilities of this tool, blueflite is searching for a researcher or engineer who can bring new expertise in control engineering and scientific computing. The focus of this work will be on machine learning methods for control. This position is part of a multidisciplinary research team, which combines expertise in artificial intelligence, feedback control, model-based and mode-free design, and security.
Main areas of responsibility:
- Lead collaborative research projects that relate to reinforcement learning for control
- Track and develop new design methods
- Implement prototype software solutions to assess relevance to Urban Air Mobility
- Work out industrial cases to demonstrate and validate the approaches
- Can work in small, agile teams with a great attitude and good communication skills
- Dedicated and creative mindset for solving complex problems
- Experience in control-based reinforcement learning methods
- Solid programming skills (e.g. Python, C++)
- Clear interest in the aerospace sector
- MSs in computer science, control engineering, physics/mathematics, or related.
- Fluent in English
- Required vaccination against COVID-19
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