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systems engineering, electrical engineering, or other relevant areas Have a good fundamental knowledge of neural networks, state-of-the-art learning algorithms, and their applications to complex systems
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
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from vascular lesions and blood, combined with genetic, clinical/epidemiological and imaging parameters from patients. We also perform in depth functional studies in animal and cell culture models
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: - Consolidated knowledge in ECC and LDPC algorithms - Consolidated knowledge in hardware description languages and hardware-prototyping toolchains (e.g., Xilinx Vivado) - Knowledge in FPGA
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: Miguel Sá Sousa Castelo Branco IV - Work Plan / Goals to be achieved: To develop and test algorithms that can provide neurofeedback in real time from neurophysiological data. V - Initial grant duration: 12
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that do not confer an academic degree, in the area or area related to that requested in the tender. Preferential factors: Have demonstrable experience in the use of machine learning algorithms applied