Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Durham University
- Harvard University
- University College Cork
- University of Southern Denmark
- Edith Cowan University
- European Space Agency
- Heriot Watt University
- King Abdullah University of Science and Technology
- MOHAMMED VI POLYTECHNIC UNIVERSITY
- McGill University
- The Ohio State University
- The University of Arizona
- UNIVERSITY OF SYDNEY
- University of Cambridge
- University of Sydney
- University of Vienna
- 6 more »
- « less
-
Field
-
for seizure detection. These algorithms will be implemented on a spiking neural network (SNN) processing unit deployed on FPGA and custom-designed chips with an integrated detection mechanism. Research area and
-
, PSIM, Proteus, LabVIEW, SketchUp, SolidWorks, etc. Knowledge of microcontrollers, STM32, FPGAs, etc. Knowledge of communication protocols such as I2C, SPI, Profibus, Modbus, CAN, MQTT, and HTTP
-
of using FPGA tools and provide them the necessary training to use the ASIC tools. The team at Cambridge consists of three investigators: Prof. Robert Mullins (PI), Prof. Timothy Jones and Dr Rika Antonova
-
multilayer PCB, FPGA programming, embedded systems, and preferably ASIC-design. Knowledge in Systems Engineering, particularly in Space and Defence is highly regarded. You will also demonstrate personal
-
communication skills. · Skills relevant to the trapping and manipulation of ultracold gases. Examples include basic electronics, development of FPGA devices, development of narrow-linewidth lasers, laser
-
turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
-
strong background in software development (Python, C++) and microscope control. • Experience FPGA programming is a beneficial. • Training and supervision will be provided throughout the project, but
-
of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard
-
provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data
-
, single-atom catalysts), analytical instrumentation (including electronics and LabVIEW FPGA programming), finite element modelling using COMSOL Multiphysics, and in-situ/operando spectroscopy, among other