Postdoc: AI for Wet-Bulb Temp from Satellites

EURAXESS Czech Republic; France; Hybride; EUR 28 000 - 34 000; Plein temps

Résumé du poste

The postdoc will work at LATMOS in the Atmospheres department, collaborating with INRIA on an ANR project to compute wet-bulb temperature from satellite data and ground stations. You will apply deep learning approaches to IASI data, co-locate satellite observations with HadISD stations, and iterate architectures for a case study focusing on India.

Regular visits to INRIA and access to IPSL computing resources will support this research, with potential access to Jean Zay.

Qualifications

• PhD in Earth/environmental sciences/remote sensing with applications in AI.
• Strong Python programming skills and experience with machine learning.
• Familiarity with Linux and scientific computing environments.

Responsabilités

• Develop deep learning approaches to estimate wet-bulb temperature from satellite data and ground stations.
• Identify infrared channels from IASI data relevant to wet-bulb temperature retrieval.
• Co-locate satellite data with HadISD ground stations and validate models.
• Present findings at conferences and prepare a scholarly paper.

Connaissances

Python

Deep learning

Remote sensing

Satellite data

Formation

PhD in Earth/environmental sciences/remote sensing with AI applications

Outils

Linux

TensorFlow/PyTorch

Description du poste

The postdoc will work at LATMOS in the Atmospheres department, collaborating with INRIA on an ANR project to compute wet-bulb temperature from satellite data and ground stations. You will apply deep learning approaches to IASI data, co-locate satellite observations with HadISD stations, and iterate architectures for a case study focusing on India.

Regular visits to INRIA and access to IPSL computing resources will support this research, with potential access to Jean Zay.