PhD position: distributed computing for UAV swarms

Informatics; Computer science » Computer systems; Mathematics » Algorithms; Environmental science » Global change; Engineering » Aerospace engineering Researcher Profile: First Stage Researcher (R1) Positions: PhD Positions Application Deadline: 16 Nov 2026 - 20:00 (Europe/Paris) Country: France Type of Contract: Temporary Job Status: Full-time Hours Per Week: 35 Offer Starting Date: 1 Sep 2027 Is the job funded through the EU Research Framework Programme? Horizon Europe – COFUND Marie Curie Grant Agreement Number: 101261439 Is the Job related to staff position within a Research Infrastructure? No Offer Description Research fields: Distributed Systems, Autonomous Systems, Environmental Monitoring Keywords: distributed computing, UAV swarms, wildfire monitoring, fault tolerance, coverage, energy efficiency, global change, remote sensing Motivation Climate change is increasing the frequency and severity of conditions conducive to wildfires across Europe, including extreme heat, drought, and prolonged periods of high fire danger. In 2025 alone, more than one million hectares burned in the European Union, representing the highest annual burnt area recorded by the European Forest Fire Information System (EFFIS) since 2006 [Eur26]. Southwestern Europe is particularly exposed, with France, Portugal, and Spain experiencing an extreme wildfire season in 2022 [Rod23] and again suffering major wildfires in 2025 [Cop26]. Given this situation, effective wildfire monitoring is becoming increasingly important, as timely and reliable information on fire fronts, temperature, smoke, and affected areas can support faster and better-informed emergency-response and civil-protection operations. This project explores how emerging technologies can improve the reliability and resilience of wildfire monitoring. Context and state-of-the-art research The increasing frequency and severity of wildfires require timely and reliable monitoring to support effective emergency response. UAVs equipped with thermal, visual, and environmental sensors provide a promising solution, as they can rapidly access dangerous or difficult-to-reach areas. Since wildfire monitoring is safety-critical, relying on a single UAV is insufficient. Instead, a UAV swarm, consisting of multiple UAVs that coordinate their actions, can provide fault tolerance and allow monitoring to continue even if some UAVs fail. Using a UAV swarm, however, raises the challenge of how to coordinate the placement and sensing activities of its members so as to maximize the monitored area while preserving the required level of fault tolerance. Recent research has explored UAV swarms for wildfire monitoring, including decentralized monitoring, fault-tolerant navigation, and coverage planning [PDM+ 24, HNC+ 22, SCB24]. However, these works mainly focus on specific algorithms and operational settings, while the fundamental relationship between fault tolerance and the amount of area that can be reliably monitored remains insufficiently characterized. This relationship is influenced by the communication requirements of the swarm. UAVs can communicate directly only when they are within communication range, forming a communication network whose structure depends on their positions. To remain connected despite up to f UAV failures, this network must provide sufficient redundant communication paths. Stronger fault-tolerance requirements therefore constrain how far the UAVs can spread apart, which in turn limits the area that can be monitored. This leads to our first research question: RQ1. Given a swarm of n UAVs and a requirement to tolerate up to f UAV failures, what is the maximum area that can be monitored while preserving the required level of fault tolerance? A second limitation of UAV swarms is their finite energy. Achieving fault tolerance requires sufficient redundancy so that monitoring can continue after failures. Maintaining this redundancy may require additional sensing, communication, movement, or standby capacity, all of which consume energy and reduce the operational lifetime of the swarm. Recent work has considered battery levels together with failures when assigning tasks to UAVs [NA26]. However, the fundamental energy cost of providing a given level of fault-tolerant monitoring remains insufficiently characterized. This leads to our second research question: RQ2. What is the minimum energy required to achieve a given level of fault-tolerant monitoring, and how can the operational lifetime of the swarm be maximized? Together, these questions capture the relationship between the amount of area that can be monitored, the level of fault tolerance that can be guaranteed, and the lifetime of the UAV swarm. References [Cop26] Copernicus Climate Change Service. European state of the climate 2025: Key events. https://climate.copernicus.eu/esotc/2025/key-events-overview , 2026. Accessed:2026-09-02. [Eur26] European Commission, Joint Research Centre. 2025 was eu's most destructive wildfire season on record. https://joint-research-centre.ec.europa.eu/jrc-news-and-updates/2025-was-eus-most-destructive-wildfire-season-record-2026-03-31_en , March 2026. Accessed: 2026-09-02. [GT07] Fabiola Greve and Sébastien Tixeuil. Knowledge connectivity vs. synchrony requirements for fault-tolerant agreement in unknown networks. In Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pages 82–91. IEEE, 2007. [HNC+ 22] Junyan Hu, Hanlin Niu, Joaquin Carrasco, Barry Lennox, and Farshad Arvin. Fault-tolerant cooperative navigation of networked uav swarms for forest fire monitoring. Aerospace Science and Technology, 123, 2022. [HVB24] Hasan Heydari, Robin Vassantlal, and Alysson Bessani. Knowledge connectivity requirements for solving bft consensus with unknown participants and fault threshold. In Proceedings of the 44th IEEE International Conference on Distributed Computing Systems(ICDCS), pages 221–231. IEEE, 2024. [NA26] Ali Nasir and Mohammad AlDurgam. Fault tolerant dynamic task assignment for uav-based search teams. Aerospace Science and Technology, 175, 2026. [PDM+ 24] Niki Patrinopoulou, Ioannis Daramouskas, Dimitrios Meimetis, Vaios Lappas, and Vassilis Kostopoulos. A distributed framework for persistent wildfire monitoring with fixed wing uavs. Drones and Autonomous Vehicles, 1(3), 2024. [Rod23] Drivers and implications of the extreme 2022 wildfire season in southwest europe. Science of The Total Environment, 859, 2023. [SCB24] Matthew Szklany, Adam Cohen, and Jayson Boubin. Tsunami: Scalable, fault tolerant coverage path planning for uav swarms. In International Conference on Unmanned Aircraft Systems (ICUAS), 2024. Where to apply E-mail: ds-resco-recruitment@lists.recherche.enac.fr Requirements Research Field: Computer science » Computer systems — Education Level: Master Degree or equivalent Research Field: Computer science » Systems design — Education Level: Master Degree or equivalent Research Field: Mathematics » Algorithms — Education Level: Master Degree or equivalent Research Field: Computer science » Informatics — Education Level: Master Degree or equivalent Skills/Qualifications In this research project, we intend to explore both fundamental and applied aspects. Candidates to this position should hold a Master's degree in Computer Science or Informatics, Mathematics or a related field by the starting date of the doctoral project. They must be excited by research in distributed systems/computing, distributed algorithms, remote sensing, applied computing to environmental sciences, and/or intermittent computing, and should have an excellent academic record in one of these areas. Familiarity with formal specification and verification (e.g., TLA+), and graph theory/algorithms would be greatly appreciated. Teamwork and communication skills are key to this position, and industrial experience is a plus. Specific Requirements Excellent proficiency in English is required (CECR : C1; IELTS : 7.0; Cambridge English Scale : 185; or equivalent). Knowledge of French is not required for this position. Languages: ENGLISH Level: Excellent Research Field: Computer science » Computer systems; Computer science » Systems design; Computer science » Informatics; Environmental science » Global change; Engineering » Aerospace engineering Years of Research Experience: 1 - 4 Additional Information Benefits This fully-funded PhD starts in September 2027 and the duration of the contract/scholarship is 3 years. Benefits include: French government strongly subsidizes its higher education system, therefore the tuition fees are among the more competitive in Europe. Social security coverage included. Dedicated funding to support research, training, networking and international mobility activities, including allowance to cover flights and living expenses for up to 12 months in Lisbon, Portugal. Subsidized meals. Partial reimbursement of public transport costs. Social, cultural and sports events and activities. Eligibility criteria Academic Requirements: By the call deadline, candidates must hold a master's degree or equivalent diploma; and must not hold a doctoral degree. Mobility Rule: Applicants must not have resided or conducted their primary activity (work, studies, etc.) in the France for more than 12 months in the 36 months preceding the call deadline. Periods of stay as a tourist, refugee, or time spent in national service, are not considered as residency. Selection process To apply, please send the following information to ds-resco-recruitment@lists.recherche.enac.fr (Subject=PhD position [ENAC-ULisboa-PhD27-BEST]: distributed computing for UAVswarms): Curriculum Vitæ Letter of motivation that should describe the applicant's background in the areas of the project, reason for interest in the project, and future plans A list of courses and grades of the last three years of study (an informal transcript is OK). Names and contact details of at least two people who can write you references, whom we will contact directly. If relevant, a link to your publications and/or open-source developments. The selection process is three-fold: First, all application will be evaluated by both project's partners Second, selected applicants will be interviewed, Finally, the ranking positions of selected candidates will be communicated as well as final hiring guidelines Additional comments About ENAC and Universidade de Lisboa The ENAC , National School of Civil Aviation, is located in Toulouse, France, the centre of the European aerospace industry (e.g., AirBus , Thales , and CNES ). It offers an ideal working environment, where researchers can focus on developing new ideas, collaborations and projects. Our research topics at ENAC Lab include emerging CPS design (e.g., drones, underwater robots and nanosatellites), aviation safety and security, sustainable transportation development, and aeronautical computer-human interactions. For further information, please consult our site . The proposed research will be developed in the ENAC research laboratory, ENAC Lab , in close cooperation with the Lagige research center of the Universidade de Lisboa in Portugal. Website for additional job details: https://recherche.enac.fr/~silvestre/jobs.html Work Location(s) Number of offers available: 1 Company/Institute: ENAC/University of Toulouse Country: France State/Province: Occitanie City: Toulouse Postal Code: 31055 Street: 7, avenue Edouard Belin Number of offers available: 1 Company/Institute: Faculdade de Ciências da Universidade de Lisboa Country: Portugal City: Lisboa Postal Code: 016 Street: Campo Grande - 1749 Contact State/Province: Occitanie City: Toulouse Website: https://recherche.enac.fr/~silvestre/jobs.html Street: 7 avenue Edouard-Belin Postal Code: 31055 E-Mail: ds-resco-recruitment@lists.recherche.enac.fr