Semantic-Comm for Vehicle Detection with Foundation Models

Ericsson; Stockholms kommun; On-site; SEK 134,000 - 201,000; Full time

Job summary

Ericsson in Sweden, Stockholm is seeking a master’s student to explore semantic communications for next-generation wireless networks. You will work with frozen foundation-model features (CLIP-ViT-B/16 or DINOv2-ViT-B/14), compress them with a learned bottleneck, and transmit over a simulated noisy channel.

A lightweight decoder will reconstruct bounding boxes and labels for six COCO classes. You will review semantic communication and joint source-channel coding, extract patch-level features,

Qualifications

• Pursuing a master's degree in Electrical Engineering, Computer Science, Telecommunications, or related field.
• Strong programming skills in Python and PyTorch.
• Solid understanding of deep-learning concepts (CNNs, transformers, training procedures, loss functions).
• Ability to design experiments, work independently, and critically interpret results.
• Fluency in written and spoken English.
• Familiarity with object detection and pretrained vision models is an advantage.

Responsibilities

• Build a semantic-communication system to extract spatial features from images using frozen CLIP-ViT-B/16 or DINOv2-ViT-B/14 models.
• Compress features through a learned linear bottleneck and transmit over AWGN channel.
• Reconstruct bounding boxes and class labels with a lightweight decoder; evaluate performance.
• Train bottleneck and decoder end-to-end with noise injection across SNR levels.
• Compare foundation-model approaches with task-specific encoder-decoder and baselines like JPEG.
• Analyze results in a communications context (mAP vs SNR, per-class performance).

Skills

Python

PyTorch

Deep learning

Independent work

English fluency

Object detection knowledge

Education

Master's degree in Electrical Engineering, Computer Science, Telecommunications

Tools

YOLO

Faster R-CNN

FCOS

Job description

Ericsson in Sweden, Stockholm is seeking a master’s student to explore semantic communications for next-generation wireless networks. You will work with frozen foundation-model features (CLIP-ViT-B/16 or DINOv2-ViT-B/14), compress them with a learned bottleneck, and transmit over a simulated noisy channel.

A lightweight decoder will reconstruct bounding boxes and labels for six COCO classes. You will review semantic communication and joint source-channel coding, extract patch-level features,