32 post-doc-in-wireless-communication-and-networks-2016 PhD positions at Umeå University in Sweden
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. Deadline for applications is 2024-04-02 Project descriptions and work tasks The projects will focus on interactions of plant communities with belowground and aboveground ecosystem processes, and their
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of injuries to the upper extremity supported by machine learning and neural networks. The position is an initiative within the framework of the research initiative "Learning and brain plasticity throughout
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Stockholm and Lindköping University. Human communication is multimodal in nature, and occurs through combinations of speech, language, gesture, facial expression, and similar signals. STING aims to design
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having an immense impact on both society at large and research especially, and this impact is expected to increase. This boom is driven by so-called deep neural networks, a class of machine learning models
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, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school
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forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school Admission requirements
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-disciplinary and international professional network between PhD-students, researchers, and industry. Read more: https://wasp-sweden.org/graduate-school Admission requirements The general admission requirements
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Stockholm and Lindköping University. Human communication is multimodal in nature, and occurs through combinations of speech, language, gesture, facial expression, and similar signals. STING aims to design
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fields. If you work as a Doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions
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structures in machine learning using geometrical principles. NODEs describe the dynamics of information propagating through neural networks in the limit of infinite depth using ordinary differential equations