PhD position [ENAC-UFPE-PhD27-BEST]: distributed computing for drone swarms

Motivation

As flash floods have become a recurring disaster[1], causing loss of life, damage to agriculture and infrastructure, and disruption to communities all over the world, more detailed modelling and monitoring of rainfall and riverine floods is key to accurately assessing the risk of increasingly frequent water-related natural hazards. Our doctoral project brings together hydrologists and computer science experts to explore modelling techniques[2,3] and novel UAV-based remote sensing methods[4,5]. The aim of this project is to improve our understanding of the phenomenon and its impacts, and to enhance the spatial and temporal resolution, particularly for localised events in densely populated areas.

Context, state-of-the-art research and scientific goals

In the face of rapid climate change, the water cycle is set to be one of the most affected of all the Earth’s ecological systems. Therefore, more detailed modelling and monitoring of rivers is key to accurately assessing the risk of increasingly frequent water-related natural hazards, such as flash floods[6] induced by rainfall. In this context, several remote sensing techniques and modelling frameworks have been employed to facilitate environmental monitoring and study the ongoing impact of human-induced climate change on hydrological cycles. A large body of research uses satellite remote sensing to provide a global perspective on the Earth. However, the intrinsic spatio-temporal resolution limitations of satellite platforms impose fundamental constraints on improving the precision of water monitoring systems.

To address these limitations, the use of cheaper, more flexible monitoring platforms based on drone swarms at very low altitudes has emerged as a promising complementary approach for collecting higher-resolution data through more flexible sampling methodologies. Indeed, airborne remote sensing using drone swarms can enable richer on-demand data collection and faster processing at the edge. This precise, high-resolution, real-time information on rapidly evolving and potentially dangerous rivers will help increase our understanding of these phenomena, leading to better forecast and, ultimately, better-informed decision-making during crisis response. While this technique shows great promise, experiments on concrete use cases are needed to better understand the advantages and limitations of drone swarms for hydrology and flood forecast.

This joint doctoral research project aims to provide an innovative, drone-based solution to the increasingly frequent problem of flash flooding and the lack of precise spatial and temporal observation resolution for surveying it. The project will provide an opportunity to design novel modelling techniques and advanced sensor technologies for emerging, very high-resolution remote sensing applications on autonomous drone swarms. This includes open research problems in mobile edge computing, environmental monitoring, urban hydrological and climate modelling, and data collection for natural hazards such as flood risk prediction and assessment. Other areas of research include wireless communication, mission planning and unmanned aircraft system traffic management and regulations for autonomous drone swarms. To this end, this interdisciplinary project combines the collection and modelling of river hydrological data with the design of fault-tolerant distributed systems for remote sensing using drone swarms.

References

[1] Tabari, Hossein. ”Climate change impact on flood and extreme precipitation increases with water availability.” Scientific reports 10.1 (2020): 13768.
[2] Lima Neto, Otacı́lio Correia, et al. ”Sub-daily hydrological-hydrodynamic simulation in flash flood basins: Una river (Pernambuco/Brazil).” Revista Ambiente Água 15 (2020): e2556.
[3] Roozbahani, Abbas, Parichehreh Behzadi, and Alireza Massah Bavani. ”Analysis of performance criteria and sustainability index in urban stormwater systems under the impacts of climate change.” Journal of Cleaner Production 271 (2020): 122727.
[4] Koutalakis, Paschalis, Ourania Tzoraki, and George Zaimes. ”UAVs for hydrologic scopes: Application of a low-cost UAV to estimate surface water velocity by using three different image-based methods.” Drones 3.1 (2019): 14.
[5] Bagnato, Alessandra, et al. ”Designing swarms of cyber-physical systems: the H2020 CPSwarm project.” Proceedings of the Computing Frontiers Conference. 2017.
[6] Dottori, Francesco, et al. ’Cost-effective adaptation strategies to rising river flood risk in Europe.’ Nature Climate Change 13.2 (2023): 196-202.

Where to apply

E-mail: rs-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: Computer science » Informatics
Education Level: Master Degree or equivalent

Skills/Qualifications

To explore both fundamental and applied aspects of this project, applicants should hold a Master’s degree in Computer Science or Informatics, Physics, Mathematics or a related field by the starting date of the doctoral project. They must be excited by research in distributed computing, distributed algorithms, remote sensing, applied computing to environmental sciences, and/or swarm robotics/intelligence, and should have an excellent academic record in one of these areas. Familiarity with formal specification and verification (e.g., TLA+), climate/complex systems modelling, 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 » Systems design, Computer science » Computer systems, Computer science » Informatics, Engineering » Systems 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 Recife, Brazil.
• 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 rs-resco-recruitment@lists.recherche.enac.fr (Subject: PhD position [ENAC-UFPE-PhD27-BEST]: distributed computing for droneSwarms):

• 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 UFPE in Brazil.

Website for additional job details: https://recherche.enac.fr/~silvestre/jobs.html

Work Location(s)

Number of offers available: 1
Company/Institute: Université de Toulouse/ENAC
Country: France
State/Province: Occitanie
City: Toulouse
Postal Code: 31055
Street: 7, avenue Edouard Belin

Number of offers available: 1
Company/Institute: Universidade Federal de Pernambuco
Country: Brazil
State/Province: Pernambuco
City: Recife
Postal Code: 50670-901
Street: Av. Prof. Moraes Rego, 1235