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Researcher in FPGA-based AI Hardware Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion
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, 120-128. Basic Qualifications A PhD (or equivalent), or near completion, in a relevant field such as chemistry, physics, materials science, or engineering (chemical, electrical, or mechanical
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obtaining, a PhD in computer science, engineering, mathematics, or a related physical sciences discipline, with research expertise in areas such as hardware-aware AI security, approximate computing, or secure
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. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power electronics, and self
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, or FPGAs control platforms. To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in
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and PhD degree certificates or equivalent (copy of original/official English translation). Complete list of publications. Publications most relevant to the position. Summary and documentation
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, 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
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postdoctoral scientist who is excited to develop a user- and developer friendly software platform for MINFLUX. • You should hold a PhD degree in computer science physics or engineering. • You should have a
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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
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, 120-128. Basic Qualifications A PhD (or equivalent), or near completion, in a relevant field such as chemistry, physics, materials science, or engineering (chemical, electrical, or mechanical