21 finite-element-methods Postdoctoral positions at UNIVERSITY OF VIENNA in United Kingdom
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biological methods are developed and applied to identify and characterize biologically active natural products. The lab consists of a motivated and international team connected to national and international
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methods Experience in acquiring third party funding Experience in teaching and supervision of students; didactic skills Excellent command of written and spoken English Team player and high social
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transnational and European history, as well as awareness of current developments and central methods in the field. They have experience with interdisciplinary theories and methods (e.g. Europeanisation, digital
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biology, microbiology or biochemistry Experience in Archaea research, including genomics and CRISPR-Cas systems Proactive and eager to learn new methods and topics Strong collaborative attitude in the lab
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knowledge of methods and theories Experience or willingness to engage in academic teaching Very good English skills and, if possible, German skills or another foreign language What we offer: Work-life balance
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experience with art-historical methods and experience with working with archives is highly desired We also expect: International scholarly publishing International lecturing activities Very good written and
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of mathematical proof automation: You identify and address research questions in the field of mathematical automation in numerical analysis or approximation theory. You implement the developed methods in Lean. You
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expected. Excellent knowledge in dealing with complex statistical models and methods and the willingness to support the team in statistical questions are also expected. The candidate should have teaching
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analysis activities for cooperation projects and clinical analysis. Engaging on the optimization of lipidomics/oxylipin (phospho) proteome analysis methods. You hold courses independently within the scope
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analysis of PDEs (with deterministic and/or stochastic methods), Gaussian Random Fields, mathematical foundations of deep learning, functional analysis and measure theory. You can find more information about