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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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world-class, mission-driven programs. Staff at the Lab teach undergraduate courses, advise graduate students, collect and disseminate world-famous digital resources on biodiversity, and engage with
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(postdoc) Limited until: 14.10.2032 Reference no.: 5906 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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fit to this project; An academic transcript of BSc, MSc and PhD education, including grades; Full Curriculum Vitae, contact information for at least two academic references, and if applicable, a list of
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. The candidates are expected to work in a highly collaborative environment with other lab members and industry collaborators. Requirements: Applicants should have a PhD in Computer Engineering, Computer
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field Computer Skills General office suite and willingness to learn lab specific programs Strong programming skills in R and/or Python Experience with Next Generation sequencing Record of peer-reviewed
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skills, especially in quantitative and/or mixed methods. Experience with statistical analysis including cluster analysis Comprehensive computer skills, with the ability to learn and utilize new and
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: biomimicry, biofabrication techniques, biomaterials, biosensors, wearable electronics, and machine learning • Experience with mechanical and rheological testing of biodegradable or soft materials • Hands