SAR–Optical Fusion for Rip-Channel Detection

Inria; Nice; Remote; EUR 6,700 - 10,000; Part time

Benefits offered by this job

Partial reimbursement of public transp

7 weeks annual leave + RTT

Teleworking possibility

Professional equipment available

Social, cultural and sports events

Vocational training

Employer-sponsored health insurance

Job summary

Inria Côte d’Azur invites a curious intern to contribute to optical–radar fusion for rip-channel detection from Sentinel data. Work with the EVERGREEN team and PhD student on BAISen2 dataset, adding Sentinel-1 SAR data and evaluating detectors using DETR architecture.

The project combines multi-temporal Earth observation, deep learning with PyTorch, and coastal applications. Candidates should have strong Python and ML fundamentals and an interest in remote sensing.

Qualifications

• Advanced Python programming skills.
• Proficiency in deep learning with PyTorch.
• Background in machine learning, computer vision or remote sensing.
• Experience with object detection is appreciated.
• Experience with SAR or satellite data is a plus.

Responsibilities

• Review the scientific literature on rip-channel and rip-current detection, SAR imagery and optical–radar fusion.
• Collect and preprocess Sentinel-1 SAR data that matches the BAISen2 dataset (same sites and acquisition dates).
• Train and evaluate DETR-like detectors on Sentinel-1 alone and compare them with detectors trained on Sentinel-2.
• Design and implement optical–radar fusion strategies in PyTorch, building on the team’s existing codebase.
• Report and summarise the results.

Skills

Python

PyTorch

Deep Learning

Remote Sensing

Object Detection

Tools

PyTorch

Job description

Inria Côte d’Azur invites a curious intern to contribute to optical–radar fusion for rip-channel detection from Sentinel data. Work with the EVERGREEN team and PhD student on BAISen2 dataset, adding Sentinel-1 SAR data and evaluating detectors using DETR architecture.

The project combines multi-temporal Earth observation, deep learning with PyTorch, and coastal applications. Candidates should have strong Python and ML fundamentals and an interest in remote sensing.