Edge AI Research Postdoc: Federated Learning & Inference

1000scholars; Valbonne; On-site; EUR 32,000 - 40,000; Full time

Job summary

1000scholars is seeking a postdoctoral researcher to work with the NEO Inria and COATI Inria teams on distributed learning, online/inference at the edge and privacy-aware ML. You will contribute to distributed training, federated learning and real-time inference research within the dAIEDGE framework.

The ideal candidate has a PhD in a relevant field, strong mathematical and ML background, and proficiency in Python with PyTorch/TensorFlow. Collaboration with European partners is expected.

Qualifications

• PhD required in Applied Mathematics, Computer Science or closely related discipline.
• Strong mathematical background in optimization, statistical learning or privacy.
• Good programming skills.
• Experience with PyTorch or TensorFlow is a plus.

Responsibilities

• Collaborate with PhD students and other researchers on the listed topics.
• Publish research results and present at conferences; contribute to deliverables of the EU project.

Skills

Mathematics background

Optimization

Statistical learning

Privacy

Machine learning

Programming skills

PyTorch

TensorFlow

Education

PhD in Applied Mathematics or Computer Science

Tools

PyTorch

TensorFlow

Job description

1000scholars is seeking a postdoctoral researcher to work with the NEO Inria and COATI Inria teams on distributed learning, online/inference at the edge and privacy-aware ML. You will contribute to distributed training, federated learning and real-time inference research within the dAIEDGE framework.

The ideal candidate has a PhD in a relevant field, strong mathematical and ML background, and proficiency in Python with PyTorch/TensorFlow. Collaboration with European partners is expected.