Research Associate in High Altitude Platforms systems

About the role

The Communications and Signal Processing Group at Imperial College has two part-time openings in machine learning (ML) and artificial intelligence (AI) to design a new layer of communication infrastructure using high-altitude platform systems (HAPS) to provide communications services to ground users. The positions are funded by the ARIA EndAP programme: https://aria.org.uk/opportunity-spaces/adaptive-machines/enduring-atmospheric-platforms/funded-projects?tabId=ta3&cardId=ai-enabled-haps. The post-holders will join a diverse team with expertise in network AI and optimization, wireless communications and networks, aircraft design and deployment regulations to leverage the strengths of different HAPS platforms, determine the best fleet composition, and coordinate platform movements for energy efficiency, resilience, and adaptability to traffic demands of ground users.

To begin, we will study how multiple HAPS platforms should be strategically moved and positioned in three-dimensional airspace to provide the optimal service coverage and meet communication quality of service (QoS) requirements for ground users subject to navigating weather, energy, and regulatory constraints. A robust backhaul network will be designed to connect HAPS with each other, and with terrestrial and satellite networks, ensuring efficient network-wide connectivity and flexibility.
We aim to develop a reinforcement-learning framework for joint optimization of air-vehicle design, real-time control, and provisioning of communication services under changing weather, energy availability, and regulatory constraints. Our goal is to develop novel distributed RL algorithms for each HAPS will function as an autonomous, distributed RL agent, making independent but cooperative control decisions to optimize its position, resource allocation, and operational strategy in real time.
This post will also participate in assessment of alternative power architectures and operational constraints to ensure consistent power availability for both individual HAPS units and entire fleets. We will develop robust fleet operational strategies to identify minimum viable fleet sizes and configurations under varying weather, deployment, and coverage requirements. Furthermore, to use the designed algorithm in practice, we will evaluate how physical constraints, such as payload size, weight, and power supply, and techno-economic tradeoffs affect the optimal HAPS system designs. We will adapt and validate the developed HAPS technologies across multiple fleet platforms.
The positions are an excellent opportunity for researcher with strong background in machine learning, wireless communications and communication networks. Strong analytic skills are expected.

What you would be doing

Research Duties include:

• To plan and carry out research in accordance with the project aims and timeframes.
• To propose and investigate new techniques, algorithms and architectures for network design, optimization and orchestration.
• To verify and characterize system performance both analytically and in realistic simulated testbeds.
• To collaborate and cooperate with other research staff both within and outside Imperial College working on related projects funded by the UK EPSRC.
• To act as the representative for Imperial College in meetings, conference calls and conferences.
• To present research at conferences, workshops, open days and meetings, and publish research results in journals.
• To develop a sound understanding of ethical and health and safety regulations, and the responsibilities of themselves and their colleagues

What we are looking for

Education

• Research Associate: Hold a PhD in Electrical Engineering, Computer Science, Mathematics, or a closely related discipline, or equivalent research, industrial or commercial experience
  *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.
  
• Research Assistant: PhD (or equivalent) candidates in Electrical Engineering, Computer Science, Mathematics, or a closely related discipline.

Experience

• Machine learning (ML) and/or optimization for communication systems
• Development, implementation and verification of ML algorithms in code
• Algorithm development and performance analysis of data-driven decision-making systems

Knowledge

• Machine learning and optimization techniques
• Key technologies in wireless communication systems
• Networked and distributed systems

What we can offer you

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
• Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Further information

Please note that job descriptions cannot be exhaustive, and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.

For any specific queries regarding the post please contact Prof Kin K. Leung (kin.leung@imperial.ac.uk)