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E-2437 – Phd candidate in floodwater mapping using ai and eo data

ABG-120132 Sujet de Thèse
08/02/2024 Contrat doctoral
Luxembourg Institute of Science and Technology
Belval - Luxembourg
E-2437 – Phd candidate in floodwater mapping using ai and eo data
  • Informatique
  • Informatique
  • Sciences de l’ingénieur
Remote sensing

Description du sujet

Temporary contract | 48 months | Belvaux/Luxembourg


Are you passionate about research? So are we! Come and join us

The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

Do you want to know more about LIST? Check our website: https://www.list.lu/

Discover our Environmental Research and Innovation department: ERIN Department


Embedded in the department’s Environmental Sensing and Modelling (ENVISION) unit, the ‘Remote sensing and natural resources modelling’ group is carrying out impact-driven research, geared towards monitoring and predicting environmental systems in a changing world. Our research group capitalizes on a blend of remote sensing data obtained from space- and air-borne platforms, as well as in-situ data measured with Internet of Things (IoT) devices, for producing information on the status of natural resources. Our research and development activities focus on the synergistic use, processing, and interpretation of data from multiple complementary active and passive sensors installed on both space- and airborne platforms. We rely on competences in environmental sciences, such as hydrology and hydraulics, meteorology, plant physiology, and geography for monitoring variations in Earth’s resources. We integrate remotely sensed information with in-situ data, process-based models and leverage satellite communication, IoT and deep learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, precision agriculture/viticulture/forestry, preservation and management of natural resources, maritime surveillance.


How will you contribute?


In the framework of the FNR-funded SWIFT project "Shallow Water Modelling and Satellite Imagery Combination for Improving Flood Prediction", we aim to advance further our capabilities in flood monitoring and modelling through the integration of remote sensing (RS) observations and shallow water modelling. We intend to leverage our expertise in remote sensing, hydraulic modelling, data assimilation, and data fusion to achieve the project's primary objectives:

  • Enhance the retrieval of topography data by combining RS-derived information from diverse satellite missions and geographical datasets.
  • Improve the accuracy of RS-based flood extent mapping by enhancing existing algorithms.
  • Identify systematic errors in RS-derived flood extent maps and improve algorithms for generating ‘exclusion maps’.
  • Enhance flood prediction by assimilating various RS-derived flood observations and leveraging improved topography, flood information, and exclusion maps.

You will:

  • Specifically concentrate on the development and validation of novel automated flood mapping systems, based on Synthetic Aperture Radar (SAR) and optical data.
  • Further develop a Deep Learning algorithm, designed to detect floodwater in urban areas and over bare soils. A current version of the algorithm uses an urban-aware U-Net model and multi-temporal intensity and coherence data from dual-polarization Sentinel-1. The urban-aware module incorporates a SAR-based probabilistic urban mask to guide the selection of input features crucial for detecting floodwater across specific land cover classes. Our goal is to extend the existing urban-aware module for including vegetated areas, thereby enabling the automated mapping of flooded vegetation.  
  • Conduct extensive background literature analysis;
  • Plan and organise experiments to define and test hypotheses and develop forefront research;
  • Publish the results of the study in peer-reviewed journals;
  • Present papers at scientific conferences;
  • Take part in the PhD and research training.

Is Your profile described below? Are you our future colleague? Apply now!


A MSc degree in e.g. mathematics, remote sensing, machine learning, engineering, computer science

Experience and skills

Some knowledge of programming and processing of remote sensing data would be an advantage.

Language skills

Good level both written and spoken English.


Your LIST benefits

  • An organization with a passion for impact and strong RDI partnerships in Luxembourg and Europe that works on responsible and independent research projects
  • Sustainable by design, empowering our belief that we play an essential role in paving the way to a green society
  • Innovative infrastructures and exceptional labs occupying more than 5,000 square metres, including innovations in all that we do
  • An environment encouraging curiosity, innovation and entrepreneurship in all areas
  • Personalized learning programme to foster our staff’s soft and technical skills
  • Multicultural and international work environment with more than 50 nationalities represented in our workforce
  • Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions
  • Gender-friendly environment with multiple actions to attract, develop and retain women in science
  • 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
  • Flexible working hours, home working policy and access to lunch vouchers

Your application must include:

  • A motivation letter oriented towards the position and detailing your experience
  • A scientific CV with contact details
  • List of publications (and patents, if applicable)
  • Contact details of 2 references


Application procedure and conditions

PhD additional conditions:

  • Supervisor at LIST: Prof. Marco Chini
  • Work location: Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
  • PhD enrolment: University of Luxembourg, Belval, Luxembourg

Candidates shall be available for starting their position in Q1 2024. Please note the universities costs are at the charge of the student. Your master diploma has to be recognized in Luxembourg. Please refer to:



Nature du financement

Contrat doctoral

Précisions sur le financement

FNR Luxembourg

Présentation établissement et labo d'accueil

Luxembourg Institute of Science and Technology

The mission of the Environmental Research and Innovation (ERIN) department is to carry out responsible impact-driven research and technological innovation & policy support for a sustainable resources management and the reduction of environmental impact of human activities, in order to enable and accelerate the transition towards a sustainable, resilient and digital economy and society in Luxembourg and abroad. The department comprises more than 200 life, environmental and IT scientists and engineers across four research units which bring together the necessary interdisciplinary knowledge and skills to tackle the major environmental challenges our society is facing today, including climate change mitigation, ecosystem resilience, sustainable energy systems, efficient use of renewable resources, and environmental pollution prevention and control.


Environmental sensing and modelling

Environmental and industrial biotechnologies

Environmental sustainability assessment and circularity

Intelligent Clean Energy Systems


Natural resources and agrosystems under change

Circular bioeconomy

Environment and health

Renewable energy systems

Sustainable cities and territories

Safe and sustainable by design production and consumption

Intitulé du doctorat

Phd candidate in floodwater mapping using ai and eo data

Pays d'obtention du doctorat


Etablissement délivrant le doctorat

Université du Luxembourg

Profil du candidat

Education Background: 

Master’s degree in e.g. mathematics, remote sensing, machine learning, engineering, computer science.

Experience and skills

Some knowledge of programming and processing of remote sensing data would be an advantage.

Language skills

Good level both written and spoken English

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