PhD Position Data Fusion and Super-Resolution for Urban Solar–Climate Modelling

Université Savoie Mont Blanc; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading research university in France is offering a PhD position focused on modeling urban radiative variables for solar energy integration. This role requires a Master’s degree in relevant fields and skills in data fusion and machine learning. The selected candidate will collaborate with an Italian university and engage in independent research within a multidisciplinary environment, while offering a gross salary of approximately €2200-2300/month.

Qualifications

• Master’s degree or equivalent required.
• Strong interest in solar energy and the urban environment.
• Advanced skills in data handling and scientific programming.

Responsabilités

• Develop physics-informed deep learning models for data fusion.
• Reconstruct high-resolution fields of solar and thermal indicators.
• Support atmosphere-aware solar cadastres.

Connaissances

Remote sensing

Signal processing

Data fusion

Machine learning

Scientific programming (Python)

Interest in solar energy

Formation

Master’s degree in energy, electronic, telecommunication, electrical engineering, computer science, physics, meteorology, or related fields

Description du poste

Organisation/Company Université Savoie Mont Blanc Department LOCIE Research Field Engineering » Process engineering Technology » Energy technology Environmental science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 15 Dec 2025 - 12:00 (Europe/Paris) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 20 Jan 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Understanding and modelling the spatio-temporal variability of radiative and thermal conditions in cities is essential for large‑scale solar integration and climate‑resilient planning. These processes depend on the interplay between urban morphology, surface materials, and atmospheric optical properties. Variations in clouds, aerosols, and thermal inversions alter the intensity and spectral composition of incoming radiation, thereby affecting photovoltaic performance and the urban energy balance. Existing Earth observation datasets remain fragmented across scales. Copernicus reanalyses (ERA5, CAMS‑Rad) provide continuous but coarse radiative fields (5–30 km). High‑resolution sensors such as Sentinel‑2, Landsat, ECOSTRESS, and SDGSAT‑1 offer fine spatial detail but sparse temporal coverage and frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative‑thermal processes at the urban scale.
This PhD will develop physics‑informed deep learning models for data fusion and super‑resolution of urban radiative variables.

The goal is to reconstruct consistent, high‑resolution fields of solar and thermal indicators (target scale: 50–100 m, daily frequency). The results will support atmosphere‑aware solar cadastres and enable refined analyses of how morphology and atmospheric modulation jointly shape the urban energy balance.

Where to apply

E-mail [e-mail]

Requirements

Research Field Engineering » Process engineering Education Level Master Degree or equivalent

Research Field Technology » Energy technology Education Level Master Degree or equivalent

Research Field Environmental science Education Level Master Degree or equivalent

Skills/Qualifications

Applicants must hold a Master’s degree (or equivalent) in energy, electronic, telecommunication, electrical engineering, computer science, physics, meteorology, or related fields. A strong interest in solar energy and the urban environment is essential, combined with solid skills in remote sensing, signal and image processing. Prior experience with data fusion, machine learning, or super‑resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific research, possess advanced skills in data handling and scientific programming (Python or equivalent).

Languages ENGLISH Level Excellent

Additional Information

The PhD will be carried out jointly (co‑tutelle) between the Université Savoie Mont Blanc (USMB) – LOCIE Laboratory (Chambéry, France), in direct continuity with the activities of the EMR‑ERMES (Énergies Solaires et Territoires) team, and the University of Genoa (UniGe) – Department of Electrical,
Electronic, Telecommunications Engineering, and Naval Architecture (DITEN) (Genoa, Italy).
The LOCIE laboratory of USMB develops advanced research on the integration of solar systems across spatial scales, linking resource characterisation, building physics, and data‑driven modelling. The research is conducted in close connection with the INES platform (Institut National de l’Énergie Solaire) and the Solar Academy network, providing a multidisciplinary environment at the interface of energy, climate, and digital sciences.
Within LOCIE, the ERMES (Énergies Solaires et Territoires) team—recognised as an Équipe Mixte de Recherche (EMR) by CNRS—focuses on the multi‑scale modelling of solar energy systems, their interaction with urban environments, and the coupling between radiative, climatic, and morphological processes.
The DITEN laboratory of UniGe brings complementary expertise in signal processing, remote sensing, pattern recognition, and energy systems optimisation. The doctoral candidate will spend between six and eighteen months at UniGe.

Selection process

• Start date: Beginning of 2026
• Supervision: Pr. Christophe Ménézo (Full Professor, USMB–CNRS), Dr. Alessia Boccalatte (Junior Professor, USMB), Dr. Martina Pastorino (Assistant Professor, UniGe)
• Duration: 3 years
• Gross salary: approx. =C2200–2300/month
• Application: Please send your application by email to [e-mail] and [e-mail] , including: (1) CV, (2) motivation letter, and (3) any recommendation letters (if available). Shortlisted candidates will be contacted to schedule an interview. Apply as soon as possible – application deadline: End of 2025.