Awarded Projects

Deep reinforcement learning (DRL) has recently emerged as a novel approach to discover efficient control strategies for active flow control (AFC).

This project will establish fundamental physical design rules for defect-induced functional materials for energy conversion and energy-efficient electronics.

The objective of this project is to design bimetallic alloys capable to act as catalysts for the Hydrogen Evolution and Oxygen Reduction Reactions (HER and ORR, respectively) by means of the application of elastic strain engineering.

Fluorescence takes place throughout the natural world. Conventional chemical wisdom proposes that in organic entities, fluorescence occurs in conjugated systems, such as the aromatics.

We plan to study point-defects in low-dimensional systems for the design and control of solid-state spin-defects for quantum technologies.

The goal of this project is to design a unified video–language grounding system towards robust understanding and tracking of object state changes over time.

Antibodies can rapidly evolve in specific response to antigens. During the affinity maturation process the immune system produces antibodies with higher specificity and affinity in response to an antigen.

This project aims to develop a domain-specific Vision Language Model (VLM) that can automatically classify operator actions in video data according to MTM-UAS standards.

The project focuses on creating Video-Based Language Models (VBLMs) that can precisely identify, measure, and categorize operator movements within manufacturing environments.