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Field
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that integrate analog/digital architectures with neuroscience-inspired algorithms Implement and optimize biologically plausible learning rules on FPGA and high-performance computing (HPC) platforms Basic
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optimization, and calibration methods. Digital logic design and implementation using FPGAs. Experience in DSP programming and interfacing. Programming in C++, UNIX, Python, Matlab, and familiarity with image
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development lifecycle Detailed design of electrical systems including circuit design, FPGA development, and low-level software development and integration Analysis of alternative solutions and trade-space
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OR related field OR physical science and experience with micro-processor, FPGA, RF, real-time automated system control, network data storage, and data acquisition Experience with general productivity
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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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virtual reality systems, code language models, high-level synthesis, Bayesian neural networks, and FPGA-based acceleration. To apply as a Research Associate you must have a PhD (or equivalent) in
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good programming and automation skills (e.g. C++, MATLAB, Python, LabVIEW, Arduino, Raspberry Pi or FPGA-based controllers) Expertise in developing robotics and motion control systems is beneficial
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accelerators and other scientific applications, focusing on FPGA-based digital signal processing for real-time control. Responsibilities span the full system lifecycle, from concept to commissioning, with
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acquisition systems for particle accelerators and other scientific applications, focusing on FPGA-based digital signal processing for real-time control. Responsibilities span the full system lifecycle, from
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ML/DL algorithms to hardware accelerators such as FPGAs. Conduct work on emulators for models simulating kilonovae and possibly black hole mergers in accretion disks. Basic Qualifications Bachelor's