Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
implied, the RIGOLETTO project will prepare the way for further exploiting the potential of RISC-V ISA (instruction set architecture) as a key technology to address the demands in the context of future
-
on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
-
electronics, which includes researchers and developers at universities and industries on both sides of the Danish-German border. The position starts on September 1, 2025, or as soon as possible thereafter
-
Job Description Are you up for the challenge of joining a highly ambitious research group involved in a joint project with international academic and industrial partners towards developing scalable
-
on the development of micro and nanotechnology-based sensors, detection systems, drug-delivery devices, and energy materials. Responsibilities and qualifications You will be responsible for the fabrication and
-
for you to kick-start your professional development in this exciting area providing new knowledge for the green energy transition. You will be among meteorologist working on understanding various
-
for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants.
-
developing and deploying an ocean‑monitoring system for offshore installations. The research will integrate optical and acoustic sensors, autonomous data collection, and advanced perception methods to track
-
Job Description If you have a key research interest in development of mRNA vaccines for a clinical relevant purpose, then this might be the right PhD position for you. The aim of the PhD project is
-
learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as