PhD Position: Resilient and Energy-Aware UAV Swarms for Wildfire Monitoring
Develop fault-tolerant UAV swarm algorithms to optimize wildfire monitoring coverage and energy efficiency under extreme conditions.
Best for:Best for early-career researchers with expertise in distributed systems, robotics, or computational algorithms seeking to contribute to climate-resilient technology.
Overview
This PhD position at ENAC’s Distributed Computer Systems and Networks lab focuses on advancing UAV swarm technology for wildfire monitoring. With wildfires worsening due to climate change, the project aims to maximize monitored area while ensuring fault tolerance and energy efficiency. The candidate will investigate trade-offs between coverage, resilience, and energy consumption in swarm coordination, addressing critical gaps in current research. The work bridges computer science, aerospace engineering, and environmental science to support emergency response systems. Applicants should have a strong background in distributed systems, autonomous robotics, or related fields.
What you'll do
- Design and implement distributed algorithms for UAV swarm coordination to maximize monitored area under fault-tolerance constraints.
- Develop energy-aware models to optimize swarm operational lifetime while maintaining monitoring reliability.
- Conduct theoretical analysis and experimental validation of proposed algorithms using simulation and real-world UAV testbeds.
- Collaborate with environmental scientists to integrate monitoring data into emergency response frameworks.
- Publish findings in peer-reviewed journals and present results at international conferences.
- Contribute to the development of open-source tools or frameworks for swarm monitoring applications.
Requirements
Degree required: Master’s degree or equivalent in Computer Science, Electrical Engineering, Aerospace Engineering, Robotics, or a related quantitative field
Degree offered: PhD
Language: English; excellent proficiency (written and spoken)
Instruction: English
- Proven expertise in distributed systems, algorithms, or robotics
- Strong programming skills (Python, C++, or similar)
- Experience with simulation tools (e.g., ROS, Gazebo, MATLAB/Simulink)
- Familiarity with optimization techniques (e.g., multi-objective optimization)
- Basic knowledge of aerospace systems or sensor networks
- Open to EU and non-EU candidates (specific eligibility criteria may apply under Horizon Europe funding)
- No prior experience cap, but 1–4 years of research experience preferred
- No age restrictions
Funding & terms
How to apply
Deadline: 2026-11-16 (Europe/Paris)
Applicants must submit a complete application package including a CV, cover letter, research proposal, academic transcripts, and two reference letters via email to the recruitment contact. Additional documents may be requested during the evaluation process.
- Curriculum Vitae (CV)
- Cover letter detailing research interests and motivation
- Detailed research proposal (2–3 pages)
- Academic transcripts (Bachelor’s and Master’s degrees)
- Two reference letters (academic or professional)
- Proof of English proficiency (if applicable)
Contact
Recruitment Contact· PhD Position Coordinator
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