Neurobus is developing cutting-edge vision solutions and systems, leveraging neuromorphic technologies to enhance the intelligence and efficiency of embedded devices and robots in the Space and Defense sectors.

We are opening an internship focused on deep learning for computer vision, with an emphasis on neuromorphic-inspired model architectures designed for efficient edge deployment. The core objective is to implement and evaluate a next-generation transformer-style vision model that operates on discrete, relative spike-timing representations and supports efficient scaling to higher-resolution inputs.

As a Deep Learning Computer Vision Intern at Neurobus, you will:

• Implement core building blocks of an efficient, transformer-style vision model in PyTorch, including components that rely on discrete/event-like computations

• Establish training and evaluation baselines on standard computer vision tasks (e.g., image classification, object detection)

• Extend the architecture with multi-scale or hierarchical processing to improve efficiency and scalability for larger images

• Benchmark performance against strong modern baselines, with attention to both model quality and efficiency metrics

• Investigate positional and spatial representation strategies suited to discrete/event-like processing

• Perform systematic ablation studies across key architectural and hyperparameter choices

• Explore regularization and robustness techniques tailored to discrete and lookup-based model components

• Document implementations, experiments, and results, and present progress updates to the team