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Field
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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initiatives at SWIMS. The ideal candidate will have expertise in IoT, sensor development, ecological monitoring, and advanced ecosystem imaging methods such as photogrammetry. The officer will collaborate
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Alex Leung (Mechanical Engineering at UCL) will also collaborate. he specialises in imaging of additive manufacturing and will support the project by assisting with the in-process monitoring. We expect
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immunology is highly advantageous. Proficiency in core immunological techniques such as multicolor flow cytometry, ELISA, tissue sectioning and imaging, or in vivo mouse models is strongly preferred
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into this environment are invited to apply. Experience in fields such as NPC, peroxisomes, DNA origami, IDPs, biophysics, single-molecule techniques, and optical imaging is welcomed. We foster diversity and female
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into this environment are invited to apply. Experience in fields such as NPC, peroxisomes, DNA origami, IDPs, biophysics, single-molecule techniques, and optical imaging is welcomed. We foster diversity and female
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, and optical imaging is welcomed. We foster diversity and female candidates are particularly invited to apply, since the gender balance recently declined with the departure of quite some female postdocs
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. Research Project & Objectives This PhD project aims to develop a field-realistic VLE prototype for education and training purposes of emergency response, construction safety and health, lean construction
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/nucleus RNAseq and ATACseq, human iPSCs, Serial Block face EM, mouse genetics, biochemistry, cell signaling control, in vivo 2-photon live imaging, and translational pharmacology to peel back the layers