13 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Umeå University
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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research in the areas of condensed matter physics, nanotechnology, photonics, and theoretical and computational physics. The research group Digital Physics (www.digitalphysics.se) develops methods
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, Python, Fortran, or comparable computational environments is highly desirable. Excellent written and oral communication skills in English are required. Knowledge of Swedish is not necessary. Merits
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, Autonomous Systems and Software Program – Humanity and Society (WASP-HS) is a national research program in Sweden. The vision of WASP-HS is to foster novel interdisciplinary knowledge in the humanities and
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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Computational Physics, Biophysics, and Plasma and Space physics. The Department is now seeking a postdoctoral researcher for a project that aims to develop luminescent carbon dots for light-emitting applications
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completed their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service
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be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice
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in radiotherapy with the goal of enabling fully adaptive radiotherapy. The work is based on deep learning, where models are trained on generated or clinical data. The project is carried out in