Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Technical University of Denmark
- University of Southern Denmark
- Nature Careers
- Aarhus University
- Technical University Of Denmark
- Graduate School of Arts, Aarhus University
- University of Copenhagen
- ; Technical University of Denmark
- Aalborg University
- Technical University of Denmark;
- ;
- Roskilde University
- 2 more »
- « less
-
Field
-
for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
-
an extrusion machine that produces large-scale earth blocks Building a 3D printer that utilizes earth materials for construction purposes Developing numerical process models that simulate 3D earth printing
-
School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 01 January 2026 or later. You can submit your application
-
: You will be responsible for the sensor system and the perception algorithms of an autonomous mobile robot. You will engage in research around deep learning and 2D/3D computer vision for a well-defined
-
gas chromatography. It is also probable that you will use CAD (e.g. for 3D printing), programming (e.g. Python) and MEMS chip design and fabrication. You should therefore have a desire to learn new
-
Center (CoaST). These facilities are now equipped with modern graphical user interfaces, unified communication protocols, and structured data pipelines that collect and store experimental data in dedicated
-
power. Your primary tasks will be to: Develop a detailed 3D multiphysics model of the HT-PEMFC stack to analyze and optimize thermal management. Design a heat recovery system, tailored
-
of eelgrass also lack other 3D structures as stone reefs to support fish and other mobile fauna, because stone fishery for constructing harbors and other infrastructure has been intensive for hundreds of years
-
Neural Networks (SSM-SNNs). The project includes the co-design and integration of a RISC-V processor for hybrid neuromorphic computing. The research aims to develop ultra-low-power computing chips
-
on conventional computing platforms such as GPUs, CPUs and TPUs. As language models become essential tools in society, there is a critical need to optimize their inference for edge and embedded systems