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deep learning models, testing and optimizing the models documenting all performed tasks in detail, visualizing the model results, and writing technical reports investigating related software and
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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. Given that current development of mathematical foundations of quantum theory draws from and needs a diverse deep understanding of all branches of pure mathematics, we offer a number of positions in all
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draws from and needs a diverse deep understanding of all branches of pure mathematics, we offer a number of positions in all branches of pure mathematics and quantum computer science at QM. We also offer
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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the theoretical foundations of quantum engineering. Given that current development of mathematical foundations of quantum theory draws from and needs a diverse deep understanding of all branches of pure mathematics
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of biosignals features and processing techniques. Experience in designing Spiking Neural Networks (SNNs) or deep learning algorithms Preferably, experience with FPGA development of SNNs ASIC design experience is
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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projects related to applying advanced analytics and/or machine/deep learning for supply chain optimisation. Note: candidates focusing primarily on Operations Research area are not an ideal fit