🐣🐥 It was exactly how it should be - a bit festive, a bit informal, and very delicious!

We are delighted to announce that a new edition of the workshops for children, organized as part of the Łódź Children’s University, has just begun. The interest in the classes has been tremendous! Initially, three sessions were planned, but all spots were filled in no time!

Prof. Paweł Strumiłło, representing our university, signed a cooperation agreement between Lodz University of Technology and Hong Kong Metropolitan University (HKMU) 🤝

The Division's research activities primarily focus on heat and mass flow modeling in electronic systems and power devices, using thermography to verify calculations. Research focuses on the modeling and design of power systems while taking into account nonlinear phenomena.

TELECOMMUNICATIONS is everywhere!! – radio – television – microwaves – telephone – internet – With us you will discover the secrets of telecommunications!!

📢 We warmly invite you to the public defense of the doctoral dissertation of our colleague 👉 Bartłomiej Sztyler in the discipline of automation, electronics, electrical engineering, and space technologies.

In February, Dr Marcin Kałuża from our Institute is undertaking a research internship at 🇬🇷 Aristotle University of Thessaloniki (AUTH) in Greece, one of the leading research centers in the region.

This academic year, exceptionally in spring - instead of autumn - we invite students and staff of Lodz University of Technology to join another edition of our flagship event promoting international mobility 🌍✈️

In their latest article, researchers from our Institute analyze how different EEG signal augmentation methods affect the performance of motor imagery classification in BCI (Brain-Computer Interface) systems.

🩺 Artificial intelligence in radiology brings both significant benefits and important risks. Language models support the analysis of medical documentation, while convolutional networks can achieve very high accuracy in precisely defined tasks such as image interpretation, detection, and assessment of pathological changes. 🧬 As a result, AI reduces variability between radiologists’ assessments, improves the reproducibility of analyses, and increases work efficiency by relieving specialists of routine tasks.