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manufacturing using single cell omicsSupervisors: Colin Clarke and Niall BarronAbout the project: The study of cellular biology has been transformed by single cell analysis. Rapid technological advances in areas
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will contribute to our ability to understand and predict this critical component of ecosystem function. You will work within group spanning ecological science, engineering, and computer science methods
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critical component analysis, and (iii) development of Automation of ML model and data selection. The applicants should have knowledge of machine learning and optical networks and willing to engage in testbed
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foster resilience among international protection beneficiaries. The methodology combines desk-based analysis across legal and ethical disciplines, with semi-structured interviews with lawmakers and
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mental health issues, using statistical techniques including network analysis. Naturally, we are seeking a student with a keen interest in the topic of loneliness and/or mental health. In addition
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that these cells could be deployed to control intracellular S. aureus. The project will employ cutting edge technologies (transcriptomics, in vivo infection models, single cell metabolic analysis) to profile
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degree (2.1 +/MSc) in mechanical / civil engineering, acoustics, materials, or related; aptitude for experimental measurements and cost-benefit analysis; creativity in sustainable design General
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nanomaterials. This will be achieved by developing and codifying DES-based routes to key classes of environmentally relevant nanomaterials, including (photo)electrocatalytic metal oxides; phosphate-based
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this project, using extensive primary cell culture, molecular analysis, scRNAseq, non-invasive imaging analysis, along with mechanistic studies using 2D/3D culture models, we will dissect the distinct cellular
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for structural variants and copy number variants (SVs/CNVs), will be used to optimize data analysis and variant discovery. The project will focus on identifying previously overlooked pathogenic variants, exploring