General information

Reference

2026-4115  

Position description

Category

F14 Information Systems - Engineer / Researcher

Job title

Postdoctorate in Machine Learning for Predictive Modeling of Mineral Fragmentation Behavior

Contract

Post-doctoral contrat

Contractual hours

Full-time

Contract duration

18 months

Context and contributions of the position

As part of the PEPR Sous-sol, bien commun program, the InnovTech project federates eight French research institutions around the sustainable processing of mineral resources, with an explicit focus on artificial intelligence and machine learning. This postdoctoral position is part of Sub-Task ST3.1.1: Closing the gap between elementary ore fragmentation properties and the performances of comminution technologies (Leader: INP/LGC - Participants: BRGM, LGF/MSE). Comminution accounts for approximately 50% of the energy consumption of world mineral processing plants. ST3.1.1 proposes a bottom-up methodology: starting from particle-scale fragmentation data generated by dedicated experimental tools and DEM simulations, coupled machine learning and phenomenological models will bridge elementary fragmentation properties and the performance of comminution machines. The ML problem is the following. Three complementary data sources characterize each mineral particle: 3D micro-CT volumes (crack networks, porosity, mineralogy), single-particle breakage test results (fracture energy, breakage probability, fragment size distributions), and DEM simulation outputs (fracture mechanisms, energy dissipation).

Job description

The objective is to learn a compact and transferable representation of particle breakage behavior, what we call the fragmentability signature, and use it to predict, at the particle scale, fracture probability, fragment size distributions, and sensitivity to loading regime, and at the process scale, energy consumption and product size distribution of comminution equipment, enabling ranked comparison of machine options for a given ore and target grind size.

As a postdoctoral researcher within InnovTech ST3.1.1, you will contribute to the development of next-generation machine learning methods for scientific and industrial data.
The project focuses on understanding and predicting the fragmentation behavior of mineral particles from heterogeneous experimental observations.
Key research directions include:
-Multimodal learning and data fusion: Develop machine learning approaches capable of integrating heterogeneous scientific and industrial data sources, including imaging, material characterization, mechanical measurements, and fragmentation experiments.
-Representation learning for complex material systems: Design compact, interpretable, and transferable representations that characterize particle properties and fragmentation behavior.
-Probabilistic machine learning and uncertainty quantification: Develop models that predict distributions and confidence estimates rather than single deterministic outputs.
-Multi-scale predictive modeling: Develop models capable of linking observations collected at different spatial and temporal scales, from material and particle characterization to fragmentation behavior and process-level performance.
-Data-efficient machine learning: Explore methodologies suited to limited, heterogeneous, and partially observed datasets, including transfer learning, self-supervised learning, surrogate modeling, active learning, and uncertainty-aware decision-making approaches.

Profile

Desired start date: January 1, 2027
Contract type: Postdoctoral Researcher
Duration: 18 months
Education: PhD

You hold a PhD in Machine Learning, Artificial Intelligence, Data Science, Applied Mathematics, Computational Statistics, Computer Vision, Scientific Computing, or a closely related discipline. You are interested in applying advanced machine learning methods to challenging real-world scientific problems.
Ideally, you should have:
-Strong expertise in machine learning and deep learning.
-Experience with multimodal learning, uncertainty quantification, probabilistic modeling or related approaches.
-Knowledge of scientific machine learning, physics-informed learning, hybrid modeling.
-Experience with image analysis, computer vision, geometric data, point clouds, graphs, or other structured data representations.
-Strong programming skills in Python and modern ML frameworks such as PyTorch, TensorFlow.
-Experience with reproducible research, model evaluation, benchmarking, and scientific software development.
-Strong communication and collaboration skills in multidisciplinary research environments.
-Experience in mineral processing would be a plus.

This position is to be filled within the DCGS/GDS unit of the BRGM at the technical center of Orléans (45) You will work closely with a multidisciplinary team of researchers from data science, applied mathematics, process engineering, and materials science. The position includes leading research activities, publishing in high-impact journals, contributing to project deliverables, and presenting results at international conferences.

Working conditions

In an environment favorable to work-life balance, in particular thanks to teleworking, the BRGM stands out for its climate of benevolence and conviviality.
Labeled HRS4R in 2021, our wish is to support you throughout your professional life within the BRGM. We provide you with numerous mechanisms to develop your skills and expertise, with possibilities for development and geography mobility.

At BRGM, you will benefit from several advantages such as a salary based on 13 months, RTT, a company restaurant, sports facilities, vacation and leisure benefits, etc.

BRGM is committed to a transparent recruitment process. To apply, please submit your application (updated CV and cover letter) by November 27, 2026.
 
At BRGM, we are committed to diversity, and all our positions are open to everyone.
 
Applications will be reviewed after the closing date of the job posting. If your application is shortlisted, we will contact you to arrange recruitment interviews with representatives of the hiring department, the Human Resources Department, and a cross-functional department.

Position location

Job location

France, Centre-Val de Loire, Loiret (45)

Location