Postdoctoral Position – 3D Point Cloud Processing and Analysis

EURAXESS Ireland; Caen; Hybride; EUR 42 000 - 54 000; Plein temps

Résumé du poste

Université de Caen Normandie invites applicants for a First Stage Researcher (R1) postdoc in Computer Science under the MODAM3 project. The position focuses on modeling and algorithms for localization and identification in 3D point clouds, using LiDAR, photogrammetry and multi-scale data.

The successful candidate will develop AI methods for analyzing 3D data, with emphasis on graph-based representations and semi-/unsupervised learning, collaborating with GREYC and affiliated teams.

Qualifications

• PhD or equivalent required for the postdoc.
• Strong programming skills in Python and/or C++.
• Experience with image processing, 3D geometry or computer vision.
• Familiarity with 3D point clouds libraries such as PCL/Open3D/PDAL is preferred.

Connaissances

Python

C++

Image processing

3D geometry

Computer vision

Formation

PhD or equivalent

Outils

PCL

Open3D

PDAL

CloudCompare

Description du poste

Organisation/Company Université de Caen Normandie Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Application Deadline 3 Nov 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Title: MODAM3: Modeling and Algorithms for the Localization, Characterization, and Identification of Objects in Massive, Multi-scale, and Temporal 3D Point Clouds

Objectives:
The MODAM3 project aims to develop new models and algorithms for the automatic localization, characterization, and identification of objects in massive, multi-scale, and temporal 3D point clouds generated from airborne LiDAR, photogrammetry, and 3D scanners. The targeted applications focus on the environment and coastal heritage, within an academic–industry consortium involving GREYC–M2C–NORM3D–ROBORATIVE.

Location: Image Team, GREYC UMR CNRS 6072

Supervisor: A. Elmoataz, Professor at the University of Caen Normandy

Project Summary
LiDAR technologies, photogrammetry, and 3D scanners now make it possible to produce enormous volumes of data describing our cities, coastlines, infrastructure, and cultural heritage. However, automatically exploiting and analyzing these data remains a major challenge.
The ModAM³ project aims to develop a new generation of artificial intelligence methods for analyzing 3D data and automatically detecting objects and changes in the environments under study.
Within this project, GREYC proposes to introduce new multi-scale graph-based representations adapted to massive and temporal data. Data processing tasks, such as localization, classification, and object recognition, will be addressed through the development of novel fundamental methods for semi-supervised and unsupervised learning. These methods will be based on new classes of partial differential equations on graphs and new graph neural network models based on diffusion processes.
The developed algorithms will be applied to several application domains, particularly cultural heritage, with a focus on Pointe du Hoc, a major historical site of the Second World war.
GREYC already has expertise in 3D reconstruction and 3D printing for cultural heritage applications, with the aim of facilitating access to and work by researchers and conservators, as well as improving accessibility for visually impaired people.

Requirements

Research Field Computer science Education Level PhD or equivalent

Skills/Qualifications

• -Python and/or C++ programming;
• -Image processing, 3D geometry, or computer vision;
• -Processing and analysis of 3D data / point clouds.

Knowledge of libraries or tools such as PCL, Open3D, PDAL, and CloudCompare, as well as machine learning/deep learning methods applied to 3D data, will be considered an asset.