Tomorrow in E-motion | PAE-e-green Days 2026

Save the Date: March 3-4, 2026 Campus Birkenfeld

About the event

We are pleased to announce the PAE-e-green Days , an inspiring event organized as part of the Interreg Greater Area project PAE-e-green.

Visit us on March 3rd and 4th, 2026 at the Environmental Campus Birkenfeld (UCB) of Trier University of Applied Sciences and discover innovative solutions for a greener future.

The PAE-e-green Days serve as a dynamic platform for scientists, policymakers, industry associations , and especially companies from the automotive sector and the supply chain . Particular emphasis is placed on the involvement of all stakeholders contributing to the electromobility value chain.

This event brings together experts, companies and visionaries to discuss sustainability, energy efficiency and green technologies .

Key topics

The participants will examine and discuss the following topics:

Energy and resource efficiency in the field of electromobility
New business models in the electrified value chain of electric vehicles
CO₂ accounting and life cycle analysis
Ecodesign and new strategies (reuse, repair, refurbishment, recycling)

Why participate?

The event offers a unique opportunity to:

Exchanging ideas with leading experts from science, industry and politics
To gain insights into innovative solutions for decarbonizing the automotive value chain
To build cross-border partnerships within the Greater Region and beyond.

Further details, including the full program and the registration link, will follow shortly.

Save the date and visit us from March 3rd to 4th, 2026 at UCB Birkenfeld!

03/03/2026 - 04/03/2026

Umwelt-Campus Birkenfeld, Campusallee 55768 Hoppstädten-Weiersbach

FR, EN, GE

Upcoming events

View all events

Horizon Europe’s EIC: From Vision to Market – Exploring EIC 2026 Pathfinder & Accelerator Challenges

The European Innovation Council (EIC) is part of the EU’s Horizon Europe programme. With a budget of over €10 billion, it supports breakthrough innovations at every stage of development.

Webinar | Information on the Law of 8 December 2025 on the renewal of the aid scheme for environmental and climate protection

The Law of 8 December 2025, which aims to renew the aid scheme for environmental and climate protection, amends the existing Laws of 20 July 2017 (establishment of a regional investment aid scheme) and of 15 July 2022 (aid scheme in the context of the greenhouse gas emission allowance trading scheme for the period 2021-2030).

L-DIH Talks | Awareness session on European Funding & Horizon Europe introduction

Unlock European funding. Accelerate your innovation journey. Europe offers some of the world’s most ambitious funding programmes supporting research, innovation, and technological advancement.

MeluXina: High Performance Data Analytics

Due to the enormous growth in the size and number of datasets, professionals need tools and techniques to work with big data. Data analysis and preprocessing of small data can be done on a single computer, most of the time in a sequential regime. Typical libraries used are NumPy, Pandas, Scikit-learn, Matplotlib.

European Defence Fund – Luxembourg national infoday 2026

Join us for the Luxembourg National Information Day dedicated to the European Defence Fund (EDF) 2026 Work Programme. This event is a collaborative initiative by Luxinnovation and the Directorate of Defence designed to help Luxembourg's industrial and research communities navigate and maximise EDF funding opportunities.

L-DIH Talks | General cybersecurity awareness

Renforcez votre vigilance. Protégez vos opérations. Développez une véritable cyber-résilience.

MeuXina: Accelerating Generative Adversarial Networks with HPC

Generative Adversarial Networks (GAN) are a type of Deep Neural Network (DNN) containing at least two networks within their structure – a Generator and a Discriminator. The Generator and the Discriminator play a competitive game against each other: The Generator tries to synthesize the most realistic data, e.g., images, videos, or signals, while the Discriminator is trained to distinguish real from fake (generated) data. The aim is to achieve equilibrium between these two neural networks, which requires a huge amount of data as well as a large amount of computation.