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. Responsibilities You will be responsible for the development of algorithms and software for data analysis take part in planning of experiments at synchrotrons actively participate in experiments Qualifications
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Job Description The Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark, Odense, invites applications for a PhD position in algorithms. The position has a
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at the section of Plant Pathology and Microbiology, where we combine fundamental and applied research within plant disease epidemiology, including evolutionary and molecular interactions between plants and
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staff scientists from the fields of macroecology, historical biogeography, oceanography, evolutionary biology, community ecology, population biology, climate change research, conservation biology and
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, the Neutral Atoms team, the Spin qubit team, the Intra- and Inter-connects team and leads within the Applications and Algorithms team. Qualifications We imagine that you are assistant professor and that you
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designing DNA, RNA and proteins to create nanoscale devices for applications in biotechnology and medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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Two postdoctoral positions (3-year) in Experimental Evolution of Methanogenic Microbiomes in Bioe...
performance and stability. You will work closely with modelling and reactor-focused colleagues to integrate evolutionary outcomes across scales. Your tasks will be: designing and running experimental evolution
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to algorithms with actionable performance guarantees. More specifically, the research will revolve around the following theme: High probability convergence in stochastic optimization under heavy-tailed noise