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Field
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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of Nottingham, and this post supports the Medical Imaging team based in the School of Medicine. In this post, you will apply advanced imaging methods, including metabolic and physiological magnetic resonance
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skills in image processing or genomic data analysis. Proficiency in rodent neurosurgeries. Familiar with Linux OS, virtual environment working and batch scripting. Programming proficiency in R, Python
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new skills in biochemistry and molecular and cellular biology, including fluorescence microscopy, live-cell imaging, ratiometric calcium imaging, transcriptomics, proteomics, mammalian cell culture
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a focus on synaptic plasticity, NMDA receptor signaling, 2-photon optogenetics, and advanced 2-photon imaging. We are particularly interested in candidates with strong expertise in whole-cell patch
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. To set up and analyze experiments and datasets under different conditions. To propose a methodology/framework in a software prototype to be developed in the project. To report research findings in a form of
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terms of stem and progenitor cell and lineage reprogramming. Studies are at the cellular and molecular level using novel molecular and imaging methodologies to understand processes and cell interactions
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in NLP. Completed a PhD or equivalent qualification or research experience in machine learning, natural language processing and image processing. Emerging track record and recognition for quality
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strategies that modulate the crosstalk between tumor and immune cells, and develop new image-based screening technologies to identify targets that mediate clinically-relevant cell-cell interactions
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etching. Use ‘zoom’ tomography and imaging to resolve structural variation across scales from 30μm down to 3nm to establish a platform for reverse bottom-up enamel remineralisation. Bottom-up multi-modal 4D