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. The PhD project is part of an interdisciplinary collaboration with the University of Bayreuth (EASI lab, led by Prof. Dr. Lisa Hülsmann). Main responsibilities include joint field work to establish an in
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underwater communications is necessary. Conventional approaches in underwater communications only develop fixed models based on human knowledge or understanding which cannot fully cover the highly dynamic and
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you like to contribute to an increased understanding of carbon cycle feedbacks in the climate system? Do you thrive in the dynamic blend of seagoing fieldwork, laboratory experiments and modelling? As a
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coating, iii) investigation of system design from small-scale to potentially pilot scale, and iv) application to micropollutant removal. Modelling aspects are open to exploration at molecular and process
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for the Endorsed Funder Route of the Global Talent Visa. We can provide an interest free loan scheme to support applicants who need to apply for a visa. Please contact Prof Yujiang Wang yujiang.wang@newcastle.ac.uk
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approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with
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backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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range of advanced functional genomics methods, particularly single-cell technologies, in pre-clinical model systems such as cell lines and patient-derived tumor organoids as well as patient samples
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the field and greenhouse. You have experience using modern approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have
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teaching at the Chair, in particular with a focus on the planning and design of railroad systems application and continued development of digital planning methods such as Building Information Modeling