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source

MINES Paris - PSL

Postdoc in Deep Learning for Water Temperature Monitoring and Prediction MINES Paris PSL in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral position lasting 9 months. Funding details such as stipend amount are not stated in the post.

Deadline

Oct 15, 2026

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Country

France

University

MINES Paris - PSL

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Keywords

Computer Science
Machine Learning
Environmental Science
Deep Learning
Hydrology
Artificial Intelligence
Earth Science
Earth Observation
Energy Transition
Climate Resilience
Physics

About this position

Postdoc opportunity at MINES Paris PSL (Ecole des mines de Paris) in the Paris/Fontainebleau area, France, focused on deep learning, machine learning, water temperature monitoring, analysis, and prediction.

The postdoctoral researcher will contribute to the Aquathermie project, developing innovative AI-based solutions for environmental and territorial knowledge in collaboration with stakeholders in the Seine basin. The work is positioned at the intersection of computer science, environmental science, earth science, hydrology, climate adaptation, and energy transition.

Eligibility highlights: applicants should have a doctoral degree or engineering degree, or proof of successful defense. Strong expertise in machine learning, especially deep learning, is emphasized. The post also asks for a CV, cover letter, two recommendation letters, a copy of the PhD/engineering diploma or defense certificate, and main scientific publications.

Duration: 9 months. Deadline: application before 2026-10-15.

How to apply: prepare a single ZIP file with the requested documents and submit it via the application link. Contact: [email protected].

Funding details

Postdoctoral position lasting 9 months. Funding details such as stipend amount are not stated in the post.

What's required

Applicants should have a doctoral degree or engineering degree, or proof of successful defense. Strong skills in machine learning, especially deep learning, are required, along with interest in using data for energy transition and territorial/environmental analysis. Main scientific publications, a CV, a cover letter, and two letters of recommendation are requested.

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