M/F design engineer specialising in data analysis and AI for the characterisation of fixed-bed catalytic reactors

Job Description

Missions

The recruited candidate will contribute to the development of a multiscale characterization and modelling methodology for granular media, applied to fixed-bed catalytic reactor configurations incorporating corrugated heat-exchange plates (pillow plates). The objective is to identify and quantify physical descriptors capable of characterizing the three-dimensional structure of particle packings and to establish relationships between this structure, reactor geometry, and transport phenomena (fluid flow, pressure drop, and heat transfer). Statistical and machine learning techniques will be employed to assess the complementarity of these descriptors, identify the most relevant parameters, and explore compact representations of particle packing structures.
The research will aim to develop predictive tools to improve the understanding of transport phenomena and contribute to the optimization of catalytic reactors equipped with pillow plates.

Activity

The research engineer will contribute to the development of a multiscale characterization and modelling methodology for granular media, applied to fixed-bed catalytic reactors incorporating corrugated heat-exchange plates (pillow plates).
The objective is to identify and quantify physical descriptors characterizing the three-dimensional structure of particle packings and to establish relationships between this structure, reactor geometry, and transport phenomena (fluid flow, pressure drop, and heat transfer). Statistical and machine learning techniques will be employed to assess the complementarity of these descriptors, identify the most relevant parameters, and explore compact representations of particle packing structures. The research will aim to develop predictive tools to improve the understanding of transport phenomena and contribute to the optimization of pillow-plate catalytic reactors.

Your Profil

Skills

Engineering degree or Master's degree in process engineering,image processing or data science. Required skills:
- Scientific programming in Python;
- Data processing and analysis;
- Basic knowledge of statistics and machine learning;
- Rigorous approach to method validation and code documentation;
- Ability to review and use scientific literature in English and to work effectively within an interdisciplinary team.

Your Work Environment

This recruitment is part of the preparation of a multiscale modelling project spanning from X-ray tomography of particle packings to process superstructure optimization, linking particle-resolved computational fluid dynamics (CFD) simulations with simplified reactor models. X-ray tomography enables the characterization of the actual spatial arrangement of particles, which influences fluid flow as well as heat and mass transfer. However, explicitly representing this geometry in CFD simulations involves substantial computational costs, limiting their applicability to reactor design and optimization. The development of a compact structural representation, combining physical descriptors and machine learning techniques, is therefore a key step in this approach. Ultimately, this representation will be used to develop closure relationships incorporated into physics-based reactor models, preserving the relevant structural information across scales while reducing the need for detailed simulations.

Constraints and risks

None

Compensation and benefits

Compensation

2521.91€ Gross Monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions