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Max Planck Institute for Dynamics and Self-Organization | Gottingen, Niedersachsen | Germany | 2 months ago
in continuous time with full mutual visibility and advance our understanding of the neural basis of human interaction. Your profile We are looking for a Postdoctoral Researcher with a PhD in Physics
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enrolled in a PhD program - art. 6º, n.4 c) https://diariodarepublica.pt/dr/detalhe/regulamento/950-2019-127238533 https://files.dre.pt/2s/2019/12/241000000/0009100105.pdf subject to suitable performance
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affiliated with the Center of Excellence Integreat . The position is for a period of four years. The nominal length of the PhD program is three years. The fourth year is distributed as 25 % each year and will
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with the Center of Excellence Integreat . The position is for a period of four years. The nominal length of the PhD program is three years. The fourth year is distributed as 25 % each year and will
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for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing
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units in neural networks, which drive both artificial and natural intelligence. Current projects span a wide range of topics in deep learning theory and theoretical neuroscience. For more information and
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: A PhD in Mathematics, Computer Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural
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they are developing Required Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience
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using computational approaches, present results at conferences and scientific meetings, prepare manuscripts for publication, and collaborate with colleagues on joint research projects, develop a research
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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve