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- Institute of Photonic Sciences
- ICN2
- Universitat de Barcelona
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Field
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research in areas such as underwater robotics, medical image analysis, and artificial perception, aiming to develop innovative technologies with real-world impact and strong links to industry and society
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mandatory) skills Protein expression (bacterial and mammalian) and purification (affinity, size-exclusion, AKTA-assisted purification) Experience with mRNA sequencing and data analysis Live imaging
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microscope that allows one to perform single-atom and single-site resolved fluorescence images of strontium Hubbard systems for the first time. Furthermore, we have very recently been able to push our setup
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(including AFM, PFM); imaging of magnetic domains (MFM); analysis of bit states for probabilistic computing (Shannon entropy, bit error rate, unclonable functions, etc.); analysis of surfaces (XPS); experience
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) participate in the monitoring at sea to acquire video and picture of the transplanted sites, (2) quantitatively analyse video to define species and functional diversity, species abundances and population size
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the discovery and optimization of high-performance functional materials through a fully integrated, interpretable AI platform. MAIAMI aims to establish a paradigm shift in materials science by embedding
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, so that their information content is known, and using imaging and genetics to characterise how that information becomes represented within individual mammalian cells. Where to apply E-mail
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images treatment Specific Requirements The aim of the project is to reconstruct the filamentary gas structures in the peripheral regions of galaxy clusters using the DisPerSE tracer, applied to Sunyaev
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well suited include soil science, agronomy, plant or environmental sciences. Experience in areas such as bioinformatics, image analysis, statistics, or modelling applied to plant-soil systems would be
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assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy fields. Part of the project will also include the development of deep learning frameworks for TEM image