Machine Learning
Deep learning, neural networks, applied models.
Building and training deep-learning models, exploring neural-network architectures and applied supervised and unsupervised methods.
Deep LearningNeural networksPyTorchTensorFlowMLOps
Key research and development directions of the AI Center — from machine learning and natural language processing to robotics and industrial digitalization. Each direction combines a scientific component with applied problems we work on together with students.
Deep learning, neural networks, applied models.
Building and training deep-learning models, exploring neural-network architectures and applied supervised and unsupervised methods.
NLP, text analysis, language models.
Analysis and generation of natural language: text embeddings, transformer language models, classification, and information extraction.
Image and audio signal recognition.
Recognition and analysis of images and audio signals, object detection, signal processing, and deploying models on edge devices.
Robotization and intelligent systems.
Intelligent robotic and automated systems: perception, control, autonomous behavior, and sensor integration.
Cloud infrastructure for AI/ML, data engineering.
Cloud infrastructure for training and deploying models, data-processing pipelines, and feature engineering for AI/ML.
AI in industry and digital transformation.
Applying artificial intelligence in industry and the digital transformation of production and business processes.
Pick a direction and join in — from a term paper or thesis to a joint research project.