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Field
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for changes to your work duties after employment. Required selection criteria You must have an academically relevant background within Learning Technologies, Interaction Design, Human-Computer Interaction (HCI
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microanalysis methods to reveal how tramp elements introduced during recycling impact microstructure and properties in aluminium mega-castings. As a PhD student, you will be supported by a multidisciplinary team
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for biological image data, scalable computer vision methods preparation of publications for submission to top-tier machine learning conferences contribution to teaching and/or supervision of Bachelor's and
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
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of recycled aluminium. More specifically, the project will focus on advanced numerical methods to understand how defects and different microstructures affect the strength of mega-cast components. As a PhD
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or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process-structure-property
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Technology, Campus Norrköping. Your work assignments This position is motivated by the need for reliable visualization and data analysis methods that support understanding of the increasing amount
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sciences Economic and Administrative sciences Maritime sciences Social sciences and Humanities. The Faculty is also responsible for PhD programmes in computer technology, innovation and regional development
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research, in and outside academia. The focus for the PhD work will be probabilistic structural lifetime estimation of offshore dynamic riser and power cable systems by use of continuous measurements
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for collecting human subject data including blood draws Must have some exposure to analyzing EEG data Must have introductory understanding of quantitative analysis methods Must be able to work independently