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Aarhus University, Department of Mathematics Position ID: MATHAU-POSTDOC3 [#28128] Position Title: Position Type: Postdoctoral Position Location: Aarhus, 8000, Denmark [map ] Subject Areas: Beyond
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with a substantial statistical component (e.g., mathematics, statistical genetics, bioinformatics). The applicants should ideally have some coding experience (e.g., in R, Stata, Matlab, C, etc), and also
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especially welcome applicants whose main strength lies in social science theory and methods and who are motivated to apply these competences to an important emerging sustainability field. Strong written and
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We are seeking applicants for a 2-year postdoc in Ultrafast X-ray probes of Quantum Materials to join us at the Department of Physics and Astronomy. Starting Date and Period The position is for 2 years and is available from August 1st or as soon as possible hereafter. There is the possibility of...
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, physics, computer science, applied mathematics, or similar Required competences Strong background in image processing and analysis, especially Deformable image registration and 3D segmentation methods
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pests, or high-throughput phenotyping Solid background in mathematics and scientific programming (R, Python, etc.) along with effective logical reasoning skills Experience with high-performance computing
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activities, as specified below (“Formalities and salary range”). Place of work Place of employment is Aarhus University, and place of work is Department of Mathematics, Faculty of Natural Sciences, Ny
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The Department of Clinical Medicine at Faculty of Health at Aarhus University invites applications for a postdoc position in the field of medical physics, within the topic image-based analysis of neurovascular and neurocognitive changes after radiotherapy of childhood brain tumours, as per May...
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—and to cooperate with colleagues on both the discrimination and paternalism theories and experimental-philosophical analyses. The position entails teaching obligations equivalent to one course per year
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, which makes it ideal for ecological simulations where precise mathematical descriptions of key processes are lacking but data for training are available. The MCL methodology will be applied on critical