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Field
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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networks using common Python-ML libraries such as PyTorch preferably also background knowledge in computational mechanics and applied mathematics highly proficient in spoken and written English We offer you
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking
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an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and
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your professional network. You will also have opportunities to present your work at leading conferences, engage in international collaborations, and develop a strong professional profile in computational
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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for: computers able to process information more like the brain, studying how to reproduce some of the properties of biological neurons and synapses using networks of molecules and nanostructures, and other
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Degree Doctorate Course location Potsdam In cooperation with The innovative PhD and postdoctoral scholarship programme is offered in conjunction with the Faculty of Digital Engineering, a faculty