Distributed AI-driven embedded intelligence ubiquitous sensing sky

Organisation/Company KU LEUVEN Department Electrical Engineering Research Field Engineering » Electrical engineering Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Final date to receive applications 23 Mar 2026 - 23:59 (UTC) Country Belgium Type of Contract Temporary Job Status Full-time Offer Starting Date 21 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number BAP-2026-48 Is the Job related to staff position within a Research Infrastructure?

No

As climate change intensifies, clear-air turbulence in aviation becomes more severe, and natural disasters occur more frequently. Even with the current typical 10-day weather forecast in the troposphere (less than 20 km), the exact spatiotemporal mechanisms through which the Earth’s low altitudes (ELA) (less than or equal to 50 km) impact weather patterns remain insufficiently understood. In-situ sensing in ELA offers an opportunity to extend weather forecasts far beyond the current 10-day range, improve ozone observation, and enhance cosmic radiation monitoring.

The rapid advancements in rotary-wing and fixed-wing unmanned aerial vehicle (UAV) technology make them a promising solution to densify ELA monitoring. However, expanding the density of meteorological sensors in the ELA to bridge the spatiotemporal meteorological gaps presents major fundamental challenges. First, the vast volume and heterogeneity of data generated by airborne UAV and Weather Balloon (WB) sensors strain efficient raw data transfer and require significant computational resources for centralized processing.

Second, the existing centralized, terrestrial-based control infrastructure cannot scale with the increasing number of airborne sensors due to bandwidth and coverage constraints. These challenges are further exacerbated due to the limited energy budget and computational resources of the meteorological aerial sensor.

In the first phase, this PhD project focuses on developing ultra-reliable spatiotemporal (4D) predictions using trustworthy, distributed AI-driven intelligence deployed across heterogeneous aerial nodes. To achieve this, an aerial platform featuring distributed computing within a mobile network of resource-constrained devices will be designed. Incorporating uncertainty-aware AI models will enhance trustworthiness and maximize resource efficiency. In the second phase, seamless integration of sensing, computation, and communication functionalities for context-aware self-organized aerial networks will be investigated.

We are looking for a highly motivated PhD researcher with an interest in optimizing distributed ML models for resource-constrained devices. The applicant should:

• have a master’s degree in Electrical Engineering or Telecommunication Engineering,
• be ranked within the top 10% of their class in MSc and BSc, and have exceptional grades,
• have strong communication skills and be fluent in English,
• have a solid background in AI-enabled signal processing and machine learning algorithms,
• have strong interpersonal skills and the ability to work in an international team.
• Experience in wireless communication and networking fundamentals is a valuable addition.

We offer:

• A PhD scholarship for up to four years (subject to positive intermediate evaluations). If the applicant is non-EER, up to 1 year of pre-doc will also be supported.
• An exciting research environment, working on the intersection between theory and implementation in a very multi-disciplinary research environment.
• A PhD title from a highly ranked university (#1 in Europe in terms of scientific innovation) after approximately 4 years of successful research.
• A thorough scientific education, the possibility of becoming a world-class researcher.
• A KU Leuven affiliation, one of the largest research universities in Europe.
• You will be working on a state-of-the-art topic with state-of-the-art laboratories.
• The possibility to participate in international conferences, workshops and collaborations with top EU research teams.

To apply , please submit the following in the online portal:

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• A CV mentioning BSc and MSc average grades.
• A motivation letter (1 page maximum)
• Complete transcripts of Bachelor and Master Degrees (if the MSc degree is not finished yet, please send the most recent transcript).
• (if available) IELTS, TOEFL or similar test proving your proficiency in English.

For more information please contact Prof. dr. ir. Jona Beysens, mail:  or Prof. dr. ir. Hazem Sallouha, mail:  .