Sort by
Refine Your Search
-
contributions to standardisation efforts. Your role This fully funded PhD position focuses on exploring and developing hybrid quantum-classical algorithms to tackle sustainability challenges. Key areas include
-
and grippers offer improved safety and adaptability but introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and
-
contribute to advancing sustainable practices in space, including: Developing techniques for debris cleansing and avoidance in autonomous space systems Smart mobility solutions and trajectory optimisation
-
This fully funded PhD position is focused on People-Centred Design (PCD) for Sustainable Construction. Your research will directly contribute to developing AI-driven tools and IoT-enabled systems that make
-
for space logistics. With the development of mathematical models and optimisation algorithms, we aim to support strategical, tactical and operational decisions in the context of the deployment of in-orbit
-
Reference Number BAP-2025-157 Is the Job related to staff position within a Research Infrastructure? No Offer Description Your work will be on the development of one or several of our current research topics
-
), consists of two main parts. First, the candidate will develop machine learning models aimed at improving the follow-up of neurocognitive function in critically ill children after discharge from the intensive
-
applications by developing pioneering innovative models and design procedures for custom-made magnetic components. The enhanced design of these components plays a crucial role in the ongoing energy transition
-
a PhD thesis in the fields of artificial intelligence, bioinformatics, and computational biology. You will develop deep neural network models for the analysis of large-scale molecular data. You will
-
. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop machine learning surrogates of wind energy systems. As newer