We are inviting applications to the Fully-funded PhD position Ref. AERO-PhD-26-01-RL_UAVs Deep Reinforcement Learning for Real-Time Trajectory Planning in Multi-Agent Aerospace Systems, with Benchmarks on Commercial Aircraft and Unmanned Aerial Vehicles (UAVs).
The duties of the PhD applicant would be:
- To pursue a PhD under the topic of “Deep Reinforcement Learning for Real-Time Trajectory Planning in Multi-Agent Aerospace Systems, with Benchmarks on Commercial Aircraft and Unmanned Aerial Vehicles (UAVs).”
- To develop different tasks in the recently granted National and European Projects (i.e., VARIANT and ZENITIA), all fully aligned with the PhD thesis topics.
- To teach laboratory/exercises classes and supervise student projects within UC3M Bachelor’s and master’s degrees in Aeronautical and Space Engineering. The teaching workload (if any) would be, in any case, low. Information on these two degrees, exclusively taught in English, is available at https://aero.uc3m.es/.
- To contribute to establishing a vigorous, internationally competitive scientific research program.
PhD position’s description:
Topic: Deep Reinforcement Learning for Real-Time Trajectory Planning in Multi-Agent Aerospace Systems, with Benchmarks on Commercial Aircraft and Unmanned Aerial Vehicles (UAVs)
Description: This PhD project focuses on developing deep reinforcement learning algorithms for real-time trajectory planning in multi-agent aerospace systems. The proposed methods aimed to be applied and benchmarked on
- Commercial aircraft (also NEXT GEN. aircraft), within the framework of the European project ZENITIA,
- Unmanned aerial vehicles (UAVs), within the framework of the national project VARIANT.
Candidates with a background in control engineering or aerospace engineering, and prior experience with artificial intelligence, optimization, and path-planning problems, are strongly encouraged to apply.
- Supervisors:
- Abolfazl Simorgh (Assistant Professor, Aerospace Engineering Department, UC3M (asimorgh@pa.uc3m.es))
- Fateme Baneshi (CTO at AI-METHODS (fateme.baneshi@aimethods.com))
The desired skills are:
- Outstanding academic record.
- Recent MSc holder (or MSc student with 60 ECTS passed at contract’s signature) with a background in Control Engineering, Aerospace Engineering, Optimization, Computer Science, Artificial Intelligence, and Environmental Sciences & Climate Change. Also, candidates with tracks in other disciplines but with outstanding academic records are invited to apply.
- International experience; teamwork and communication skills.
- Ability to deal independently with scientific and engineering challenges, mainly in innovative, interdisciplinary technologies
The contractual conditions are:
- 4-year contract.
- Annual gross salary: 22000-24000€ range (salary supplements may be awarded by UC3M internal calls)
- Become part of a young, dynamic, highly qualified, collaborative team.
- Flexible working environment and schedule.
- Opportunity to travel to international conferences (Europe and overseas) and present research activities, including research stays in top universities.
- Laptop and access to the Spanish national PhD scholarship calls.
- Health coverage under the National Health System.
How to apply:
Interested candidates must send their applications to Abolfazl Simorgh (asimorgh@pa.uc3m.es), indicating in the e-mail subject the reference code (i.e., Ref. AERO-PhD-26-01-RL_UAVs) of the position, including:
- a CV (max. 4 pages).
- a motivation letter of experience, interests, and future goals (max. 1 page)
- The contact information for two professional references.
Submission of applications is due by June 20th, 2026 (though early applications are strongly encouraged). The selection process is expected to be completed in July 2026, and the contract is to begin in September 2026 (though it might be earlier/later if agreed).
PhD in Aerospace Engineering UC3M 