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Artificial Intelligence Specialist for Autonomous Systems – KTP Associate
| Dyddiad hysbysebu: | 17 Mehefin 2025 |
|---|---|
| Cyflog: | £40,000 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 23 Mehefin 2025 |
| Lleoliad: | CV31JL |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | Aston University |
| Math o swydd: | Dros dro |
| Cyfeirnod swydd: | 0047-25 |
Crynodeb
This Knowledge Transfer Partnership (KTP) project aims to develop an advanced, decentralised software platform to optimise Aurrigo’s fleet management for airport baggage/cargo-handling autonomous vehicles. The project will transition operations from human-controlled systems to an autonomous command-and-control structure.
By integrating cutting-edge multi-agent systems, federated learning, and game theory, this project will develop sophisticated decentralised algorithms that enable autonomous vehicles (AVs) to intelligently delegate tasks, plan activities, and coordinate actions with minimal message passing. The outcome will be a more efficient, resilient, and scalable AV system, reducing human intervention and improving operational efficiency.
This is a unique opportunity to work at the forefront of AI and autonomous systems, bridging academia and industry to create real-world impact.
Candidate Profile: MSc (essential) / PhD (desirable) in Computer Science, AI, or a related field.
Essential skills/ experience required include:
• Strong Understanding of machine learning algorithms, including deep and federation learning, particularly their application in autonomous systems.
• Expertise in multi-agent systems and decentralised coordination.
• Experience with simulation techniques for complex systems.
• Strong analytical and problem-solving skills, with the ability to conduct high-quality independent research.
• Project management skills, including familiarity with Agile methodologies and tools.
• Strong coding ability, with experience in relevant programming languages (e.g. Python, C++, or MATLAB)
Desirable:
• Industrial experience in autonomous vehicle systems or similar technological environments.
• Experience with Partially Observable Markov Decision Processes (POMDPs) and their decentralised applications.
• Familiarity with Game Theory principles, particularly in competitive scenarios.
• Knowledge of Federated Learning and its implementation in distributed systems.
Main responsibilities:
• Research, develop, and optimise machine learning algorithms, including deep learning, for AV control and coordination.
• Apply multi-agent systems to enhance decision-making and task delegation.
• Integrate and test algorithms within Aurrigo’s software and control systems.
• Conduct evaluation through simulation and real-world testing.
• Contribute to dissemination, reporting, and collaboration with industry and academic partners.
• Support project management and adapt to evolving priorities.
Personal attributes:
• Strong interpersonal, communication and presentation skills.
• Leadership qualities, resilience, autonomy in organisation, and time management skills to deliver the KTP project and ensure it achieves planned milestones and objectives.
• Ability to work independently and use initiative.
Additional Benefits and support:
• Up to £6,000 for personal and professional development for the duration of the project
• Annual leave (25 days p/a)
• Professional support and mentorship
• Mental Health and wellbeing support: https://www.aston.ac.uk/staff-public/hr/Benefits-and-Rewards/health-wellbeing
By integrating cutting-edge multi-agent systems, federated learning, and game theory, this project will develop sophisticated decentralised algorithms that enable autonomous vehicles (AVs) to intelligently delegate tasks, plan activities, and coordinate actions with minimal message passing. The outcome will be a more efficient, resilient, and scalable AV system, reducing human intervention and improving operational efficiency.
This is a unique opportunity to work at the forefront of AI and autonomous systems, bridging academia and industry to create real-world impact.
Candidate Profile: MSc (essential) / PhD (desirable) in Computer Science, AI, or a related field.
Essential skills/ experience required include:
• Strong Understanding of machine learning algorithms, including deep and federation learning, particularly their application in autonomous systems.
• Expertise in multi-agent systems and decentralised coordination.
• Experience with simulation techniques for complex systems.
• Strong analytical and problem-solving skills, with the ability to conduct high-quality independent research.
• Project management skills, including familiarity with Agile methodologies and tools.
• Strong coding ability, with experience in relevant programming languages (e.g. Python, C++, or MATLAB)
Desirable:
• Industrial experience in autonomous vehicle systems or similar technological environments.
• Experience with Partially Observable Markov Decision Processes (POMDPs) and their decentralised applications.
• Familiarity with Game Theory principles, particularly in competitive scenarios.
• Knowledge of Federated Learning and its implementation in distributed systems.
Main responsibilities:
• Research, develop, and optimise machine learning algorithms, including deep learning, for AV control and coordination.
• Apply multi-agent systems to enhance decision-making and task delegation.
• Integrate and test algorithms within Aurrigo’s software and control systems.
• Conduct evaluation through simulation and real-world testing.
• Contribute to dissemination, reporting, and collaboration with industry and academic partners.
• Support project management and adapt to evolving priorities.
Personal attributes:
• Strong interpersonal, communication and presentation skills.
• Leadership qualities, resilience, autonomy in organisation, and time management skills to deliver the KTP project and ensure it achieves planned milestones and objectives.
• Ability to work independently and use initiative.
Additional Benefits and support:
• Up to £6,000 for personal and professional development for the duration of the project
• Annual leave (25 days p/a)
• Professional support and mentorship
• Mental Health and wellbeing support: https://www.aston.ac.uk/staff-public/hr/Benefits-and-Rewards/health-wellbeing
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