Postdoc: Distributed Foundation Model Inference (Edge-Cloud)

Communication, Control and Automation; Favrskov Kommune; On-site; DKK 420,000 - 520,000; Full time

Job summary

Aarhus University, Department of Electrical and Computer Engineering, invites applications for a 2-year postdoctoral position in adaptive and distributed foundation model inference across the computing continuum. The project focuses on scalable, low-latency, resource-aware deployment of large foundation models exploiting Edge and Cloud resources.

The successful candidate will contribute to cutting-edge distributed inference strategies, MoE placement, and compression techniques, with

Qualifications

• Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field.
• Strong background in Deep Learning (e.g., Transformers, foundation models).
• Strong programming skills in Python and experience with deep learning frameworks (e.g., PyTorch).
• Experience with distributed systems and edge AI.
• Strong publication record in reputable conferences or journals relative to career stage.
• Excellent written and spoken English communication skills.

Responsibilities

• Contribute to cutting-edge research on distributed foundation model inference.
• Investigate MoE placement and scheduling for inference acceleration.
• Advance token compression or intermediate feature compression and resource-aware inference optimization.
• Publish results in top-tier venues and collaborate with partners within Horizon Europe project.

Skills

Deep Learning

Python

PyTorch

Distributed systems

English communication

Publication record

Education

Ph.D. in Computer Science/Engineering

Tools

Python

PyTorch

Job description

Aarhus University, Department of Electrical and Computer Engineering, invites applications for a 2-year postdoctoral position in adaptive and distributed foundation model inference across the computing continuum. The project focuses on scalable, low-latency, resource-aware deployment of large foundation models exploiting Edge and Cloud resources.

The successful candidate will contribute to cutting-edge distributed inference strategies, MoE placement, and compression techniques, with