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. PhD Candidate in advanced immune competent liver models Apply for this job See advertisement About the position The Hybrid Technology Hub CoE (https://www.med.uio.no/hth/english/ ) is inviting
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Xomalis. About the project We are searching for a creative, skilled and ambitious candidate for our activities on the optics of wearable metasurfaces with applications in health monitoring. The aim
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for analysing authentic, synthetic, and manipulated images or videos. The work will combine predictive performance with explainability, uncertainty estimation, robustness, and generalization. Attention will be
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
sector for accelerated and successful use of AI. To demonstrate and prototype maritime AI solutions, the center will address a diverse and impactful set of use cases: Logistics, shipping and business
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The Hybrid Technology Hub CoE (https://www.med.uio.no/hth/english/ ) is inviting applications for a three
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. The department conducts research and education in 1) sea-ice-air interactions, including oceanography, cryospheric science and meteorology and 2) space physics. The department operates a world-class optical
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based tissue imaging. The work also includes functional characterization of genetic variants identified in patient cohorts, as well as testing of immunomodulatory drugs that affect IL 12/IFN γ signalling
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monitoring 96 parallel cell-culture experiments under precisely controlled environmental conditions. The system integrates robotics, automated sample handling, sensor networks, imaging systems, cloud computing
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PhD in Advanced Testing, Modelling, and Simulation of Composite Materials under High Velocity Impact
positions for general criteria for the position. Preferred selection criteria Experience in solid mechanics and non-linear finite element analysis. Experience in optical measurements related to experimental
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system for ferry quays, containing labelled damage data as training material for a machine learning model capable of automatically classifying structural damage from drone inspection images. The project