진학프로석·박사 채용 접수 플랫폼

The work associated with this position is part of the Previzo project, whose objective is to develop an artificial intelligence based decision support tool to optimize water resource management, in particular by anticipating water related crises and conflicts of use. In a context of climate change, ecosystem eutrophication, and alterations in hydrological regimes, algal blooms defined as massive proliferations of algae or cyanobacteria in surface waters represent a major environmental and public health challenge, affecting water quality, aquatic biodiversity, and human uses. The research work will focus on the detection and prediction of these algal blooms in surface water bodies using multispectral remote sensing imagery, through the development of frugal artificial intelligence models capable of efficiently exploiting heterogeneous data while minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and improved anticipation of algal bloom events. The research will be conducted within the Image & Vision research axis of the Prisme laboratory, in a multidisciplinary research environment at the interface of artificial intelligence, Earth observation, and environmental sciences.

The PRISME laboratory is a research unit jointly affiliated with the University of Orléans and INSA Centre Val de Loire (UR 4229). Its scientific scope and methodological approaches fall within the scientific field of Science and Technology (ST). They encompass both Engineering Sciences and Information, and Communication Sciences and Technologies.

The PRISME laboratory has a multidisciplinary vocation within the broader domain of engineering sciences and technologies, covering a wide range of disciplinary fields including combustion in engines, energy systems, explosions, aerodynamics, fluid mechanics, signal and image processing, control systems, and robotics.

The postdoctoral researcher will design and develop frugal artificial intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in surface freshwater bodies, evaluating model performance and robustness across different hydrological contexts, and optimizing computational efficiency to ensure low energy and resource consumption. The researcher will also be responsible for the deployment and integration of the developed models and methods in collaboration with project partners, as well as for data preprocessing, model validation, result interpretation, and the dissemination of research outcomes through scientific publications and project reports.

PhD in computer science, artificial intelligence, computer vision, image processing, remote sensing, or a related field. The candidate is expected to be able to design, implement, and evaluate artificial intelligence models, as well as to optimize their computational efficiency. Strong programming skills are required, particularly in Python and machine learning libraries, along with experience in data management. Experience in model deployment or the development of operational tools will be considered an asset. The ability to interact with various project partners and to adapt to an applied research environment is highly valued.