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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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test planning, instrumentation (e.g., strain gauges, LVDTs, DIC), execution of large-scale tests, and data analysis. Solid understanding of structural behavior, failure mechanisms, and durability issues
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total of approximately 1,400 employees and 17,700 students spread across two inspiring campus environments in Karlstad and Arvika. More information at: kau.se/en/work-with-us Description Due to
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microorganisms, and developing of spectral collection and analysis protocols that will allow this biochemical data to be effectively used to support optical microscopy-based deep-learning algorithms for species
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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design, data collation and analysis, as well as write and publish scientific papers in collaboration with the research team. The postdoc is also expected to contribute to team activities, assist in student
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share and co-finance common resources (e.g., IT and an applied biostatistics group). The department is situated at campus Solna. Further information can be found at http://ki.se/en/meb Division
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-unique CO2 experiments, cutting-edge NMR spectroscopy and isotopomer analysis (doi: 10.1111/nph.20358). Two postdocs will work together to conduct plant ecophysiology experiments, and to analyze samples by
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experiments, samples from world-unique CO2 experiments, cutting-edge NMR spectroscopy and isotopomer analysis (doi 10.1111/nph.20358). Two postdocs will work together to conduct plant ecophysiology experiments
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dynamical systems theory, including differential equations, simulation techniques, state-space and input-output representations, time-delay embedding, and/or time series analysis from experimental data