Data Assimilation for Predicting Pollution Maps Under Realistic Environmental Conditions

il y a 5 jours

RueilMalmaison, France IFP Energies nouvelles Temps plein

**Data assimilation for predicting pollution maps under realistic environmental conditions**:

- Réf **ABG-134951**
- Sujet de Thèse
- 09/01/2026
- Contrat doctoral
- IFP Energies nouvelles
- Lieu de travail- Rueil-Malmaison - Ile-de-France - France
- Intitulé du sujet- Data assimilation for predicting pollution maps under realistic environmental conditions
- Champs scientifiques- Physique
- Mots clés- Data assimilation, Ensemble Kalman filter, pollutant dispersion, LBM, CFS, Large-eddy simulation, industrial safety

**Description du sujet**:
Controlling pollutant emissions is one of today’s most pressing environmental challenges. Greenhouse gases (GHGs) and industrial emissions such as methane (CH₄) and hydrogen (H₂) directly impact air quality, safety, and climate change. Yet predicting pollutant dispersion in complex environments like industrial sites remains difficult due to fluctuating wind conditions and obstacles.
This PhD strives for strong scientific impact, contributing to global emission monitoring and decarbonization efforts. Results will be shared through high-level publications and international conferences, ensuring excellent visibility within both academy and industry.

**Prise de fonction**:

- 02/11/2026

**Nature du financement**:

- Contrat doctoral

**Précisions sur le financement**:
**Présentation établissement et labo d'accueil**:

- IFP Energies nouvelles

IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see our WEB site.
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.

**Site web**:
**Academic requirements**University Master degree (or equivalent) in Mathematics, Computer science or Fluid mechanics
**Language requirements** English level B2 (CEFR), French or willingness to learn French
**Other requirements** CFD, Programming skills (Python, C++), numerical analysis, turbulent flows

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