Living Labs
AIM Lab
AIM Lab (Artificial Intelligence in Manufacturing Lab) was established in September 2021 as a strategic partnership between the Faculty of Electrical Engineering and Computer Science at VSB-TUO and ŠKODA AUTO. The laboratory serves as a unique research and educational hub in Industry 4.0, bridging the gap between innovative academia and real-world industrial practice.
Our core focus lies in applied artificial intelligence, machine learning, advanced industrial data analytics, and mathematical optimization for manufacturing and logistics. The team concentrates on predictive maintenance models, storage and production simulations, industrial data mining, and smart mobility technologies.
Beyond applied R&D, AIM Lab plays a vital role in education by training students across bachelor’s, master’s, and doctoral programs through hands-on industrial projects. By turning theoretical methods into deployable solutions, the lab actively accelerates digital transformation across the modern industrial ecosystem.
Industry
Services & Equipment
Our cooperation includes:
AI readiness and feasibility audits
Consulting audits that evaluate data, infrastructure, and use-case maturity, then propose a practical roadmap for deploying AI in the partner organisation.
Computer vision and automated inspection
Applied computer vision for manufacturing quality control and operational monitoring, including classification, tracking, and automated visual inspection workflows.
Consortium grant development
Support for building consortia and preparing proposals under schemes such as TA ČR and Horizon Europe, from concept to project execution.
Corporate training and technical workshops
Education for industrial partners, from practitioner workshops to structured training in machine learning, data mining, and neural-network methods.
Custom predictive and analytical modeling
Contract R&D covering custom predictive models for industrial and commercial partners, from architecture design through training and evaluation on partner data.
Exploratory data analysis and industrial ETL
Data-engineering support that turns heterogeneous industrial data into structured datasets suitable for modelling, monitoring, and production ML pipelines.
Lifelong and online machine learning
Methods for continual learning from data streams, including concept-drift detection and memory architectures that prevent catastrophic forgetting in production models.
NLP and large language models
The lab deploys and adapts generative language models on local infrastructure so that company data stay on-premise, including retrieval-augmented generation and semantic analysis of internal documents.
Predictive maintenance and telemetry dashboards
Algorithms and dashboards for industrial telemetry that detect anomalies, estimate remaining useful life, and report operational status in real time.
Process simulation and logistics twins
Simulation of storage, production, and logistics flows so partners can evaluate layout and sequencing decisions before physical deployment.
Trajectory and robotics optimization
Industrial optimization of robotic trajectories using computational geometry, machine learning, and multi-objective methods to raise transfer efficiency and shorten cycle times.
Equipment & facilities
High-Performance AI Computing Cluster
High-VRAM nodes: 2× NVIDIA RTX A6000 (48 GB GDDR6 ECC) per node, 96 GB total VRAM per server — for large transformer training, memory-intensive batch jobs, and complex fine-tuning. High-throughput nodes: 2× NVIDIA GeForce RTX 4090 (24 GB GDDR6X) per node, 48 GB total VRAM per server — for rapid prototyping, computer vision, genetic algorithms, and high-frequency inference.
On-premise LLM software stack
Standard ML tooling (Python virtual environments, Scikit-learn, JupyterLab) plus an integrated framework for running on-premise generative models using Ollama and Open WebUI.
Tiered storage architecture
Ultra-fast scratch storage: 12 TB local NVMe for active dataset streaming during training. Local persistent storage: 12 TB SSD for working directories, code, and checkpoints. Network storage: over 1.3 PB combined capacity (2× 679 TB) for shared datasets, backups, and archiving.
Projects
AI Applications for Mitigating the Psychological Impacts of Natural and Environmental Events
Development of Advanced Predictive Software with AI for Retailers and Suppliers
Lifelong Machine Learning from Data Streams (CEUS Lead Agency)