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Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position – Ad Print4Life DC14 Reference code: 50156137_2
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Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM) - Universidad Politécnica de Madrid (UPM) | Spain | 2 days ago
Research Infrastructure? No Offer Description Master’s degree in Agronomy. Research Line: Image analysis and early detection of pests and diseases in greenhouse crops. Where to apply E-mail ceigram.etsiaab
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the body under the novel communication paradigm called Internet of Bio-NanoThings, where sensing, actuation, and computing processes are tightly coupled to pervasively, perpetually, and precisely sense
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research prototype, a system designed to integrate GDPR compliance directly at the operating system level. Main tasks : - Getting familiar with the rgpdOS prototype - Designing BPF functions to extend
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performance; Perform and Analyse Particle Image Velocimetry data in shear controlled continuous experiments; Developing guidelines for controlling negative effects of SACs in anaerobic wastewater treatment
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. PhD Candidate Neuroimaging in Epilepsy Our goal: to leverage advanced Magnetic Resonance Imaging (MRI) techniques (including functional MRI to assess brain network metrics and MR spectroscopy to measure
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against CMV infection and disease and develop innovative vaccine concepts. You will use state of the art virus mutagenesis, real-time imaging of virus infection dynamics, multiparametric flow-cytometry and
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embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy) to evaluate performance
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imaging techniques (<3 months=5 points, 4-12months=10 points, >15 months=15 points) 15 5. Laboratory experience in C.elegans genetics. (<3 months=5 points, 4-12months=10 points, >15 months=15 points) 15 6
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will focus on integration of the extensive amount of data collected by forest machines with remote sensing data to further characterize forest structure. Remote sensing data, such as images, lidar, and