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Field
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period of 3 years. The position is subject to external financing through the RCN funded project "Quantum Oscillator Networks for Optimisation and Machine Learning" (project number 358752). About the
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areas: wireless security, wireless communications, AI/machine learning for communications, covert communications, signal processing, and/or RF design. Ability to conduct research independently and
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Skills, Qualifications & Requirements: Required Qualifications The ideal candidate will hold a PhD in the field of psychology, neuroscience, computer or data science, physics, or physiology with brain
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Research Center for Molecular Medicine (CeMM), ÖAW | Graz 12 Bez Andritz, Steiermark | Austria | 3 months ago
; and how these mechanisms can be understood, modelled and ultimately perturbed for biomedical discovery. Two scientific tracks Track 1: Computational Biology / Machine Learning for membrane protein
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a machine learning model for storm surge prediction. Key Responsibilities Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure
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. Experience with statistical modelling, signal processing, or machine learning. Interest in active perception, surface exploration, and computational neuroscience. Eligibility criteria Applicants must satisfy
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system. Job Requirements: Possess a PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Solid background in
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relationships and apply data-driven or machine learning approaches to guide molecular design and accelerate materials discovery Collaborate with internal and external stakeholders, including computational, data
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publications at major machine learning, AI or closely related conferences and journals; and (b) good communication and writing skills in English. Applicants are invited to contact Prof. Qiu Anqi via email at
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research in various areas of mobile network systems, multimedia and AR/VR/XR systems, robotics and machine learning, focusing on fundamental aspects as well as on applications in multidisciplinary contexts