Research Associate in High Altitude Platforms systems

Job number

ENG04069

Faculties

Faculty of Engineering

Departments

Department of Electrical and Electronic Engineering

Salary or Salary range

£50,733 - £59,484 per annum

Location/campus

South Kensington Campus - Hybrid

Contract type work pattern

Annualised Hours - Part time Fixed term

Posting End Date

26 Oct 2026

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 ([email protected])

Available documents

Attached documents are available under links. Clicking a document link will initialize its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert before the stated closing date, should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.

If you encounter any technical issues while applying online, please don't hesitate to email us at [email protected]. We're here to help.

About Imperial

Welcome to Imperial, a global top-ten university where scientific imagination leads to world-changing impact.

Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world’s toughest challenges. Whatever your role, your contribution will have a lasting impact.

As a member of our vibrant community of 22,000 students and 8,000 staff, you’ll collaborate with passionate minds across nine London campuses and a global network.

This is your chance to help shape the future. We hope you’ll join us at Imperial College London.

Our Culture

We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our web pages.

Proud signatory of the Armed Forces Covenant. We welcome applications from the Armed Forces community.

Our values are at the root of everything we do, and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.