Sort by
Refine Your Search
-
Category
-
Country
-
Program
-
Field
-
, SystemC), reconfigurable architectures (e.g. FPGA, CGRA) What we expect from you: above-average degree achieved in short study period willingness and ability to think beyond the boundaries of your field
-
-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
-
in advanced firmware design for state-of-the-art FPGA architectures. The role involves working with industry-standard tools (e.g., Intel Quartus or AMD Vivado, Modelsim/Questa), implementing high-speed
-
/linux shell. Must include familiarity with a terminal-based text editor. One year or more of experience with firmware development with FPGAs. Experience working with bit-level operations on files(e.g
-
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
-
hardware systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
-
hardware systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
-
-power edge AI, microcontroller architectures, FPGA-based systems and VLSI, low-power and resource-constrained systems and analogue and mixed-signal systems. Publish high-quality outputs, pursue external
-
-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
-
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