Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Fraunhofer-Gesellschaft
- ;
- Cranfield University
- Ghent University
- Lulea University of Technology
- University of Nottingham
- ; The University of Edinburgh
- AALTO UNIVERSITY
- Chalmers University of Technology
- DAAD
- Forschungszentrum Jülich
- Nature Careers
- University of British Columbia
- University of Twente
- ; Anglia Ruskin University
- ; Cranfield University
- ; Loughborough University
- ; The University of Manchester
- ; University of Bristol
- ; University of Leeds
- ; University of Nottingham
- Duke University
- Linköping University
- Ludwig-Maximilians-Universität München •
- Monash University
- NTNU - Norwegian University of Science and Technology
- National Research Council Canada
- Radboud University
- Technical University of Denmark
- Technische Universität Berlin
- The University of Chicago
- University of Nebraska–Lincoln
- University of Oxford
- University of Southern Denmark
- Yeshiva University
- 25 more »
- « less
-
Field
-
into the co-design of ultra-low-power AI hardware architectures tailored for edge computing applications. The research aims to develop neuromorphic processors, FPGA/ASIC-based AI accelerators, and intelligent
-
shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
-
(Hardware for Artificial Intelligence) Reference Number: IV-151/25 Are you excited about designing hardware that mimics biological intelligence with the aim to explore and understand how the brain works? Are
-
intelligence through hardware design. To this end, both the hardware foundation and the underlying hardware are continuously being developed. The design flow of these circuits and their (sub)systems is of
-
(coordination) and safety constraints can be intractable. Your work will bridge this gap by providing generalizable, provable design approach that apply across a wide range of networked systems. This 3.5-year PhD
-
clinical stakeholders and system engineers. This research does not centre on the hardware design but on the technologies and systems that support its use, including AI, embedded interfaces, and mixed-reality
-
electron microscopy (4D-STEM) techniques. The Research Associate will design and execute in-situ experiments under liquid nitrogen and liquid helium conditions, including developing the hardware and
-
-driven runtime detection-based mitigation. The candidate will design techniques for automated assessment of attack surfaces and vulnerabilities across software/hardware layers.
-
Everyone is talking about artificial intelligence. But who is developing the necessary chips? We are, for example! Would you like to help drive the development of a new highly efficient AI hardware
-
solutions. The successful applicant will work with mechatronic systems, including high accuracy test machinery and will be expected to develop confidence with the design and implementation of new experimental