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Field
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important question to solve, as DNA damage-stalled RNA polymerase causes bigger problems for the cells than the actual DNA damage itself. In this project we will use innovative single molecule imaging
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. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance of electrodes and to map electrolyte chemical composition in
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. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance of electrodes and to map electrolyte chemical composition in
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22 Sep 2025 Job Information Organisation/Company Universidade de Coimbra Department Department of Informatics Engineering Research Field Engineering » Computer engineering Researcher Profile First
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Description Two PhD positions in atmospheric selenium research: field & lab analyses and process-based modeling We are seeking two highly motivated and curious PhD students to join the Inorganic Environmental
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using the KM3NeT detectors • Data analysis from SVOM instruments as well as images from the COLIBRI telescope • Participation in KM3NeT shifts and service tasks (calibration, construction, processing), as
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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, patch clamp recording, confocal microscopy, calcium imaging and other biochemical analysis (western blot, ELISA, etc.). Ability to work independently and collaboratively in a team environment Excellent