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. These systems will serve as model platforms for studying high-entropy crystallisation, offering microscopic insight into how molecular-scale parameters control emergent order. Robotic deposition and high
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diversity. We work to identify the genes that regulate plant development, describe the evolutionary histories of these genes, and connect the molecular evolution of developmental genes to the evolution
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freshwater fishes, structured around the following objectives: Use the LOC to map the freshwater fish distributions in Madagascar, including threatened, invasive and human food species Create predictive models
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Application deadline: All year round Research theme: Multi-scale modelling How to apply:uom.link/pgr-apply-2425 No. of positions: 1 This 3.5 year PhD project is fully funded for home students
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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tasks for the position The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer
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Department:Biochemistry and Molecular Biology Salary Salary:Commensurate Position Details Full/Part Time Status:Full Time Percent Time:100% Position Description: TITLE: POSTDOCTORAL RESEARCHER (Wallrath and Drack
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(or equivalent) in biological sciences. Strong research background in cell biology, molecular biology, mouse models of cancer, and/or biochemistry. Prior experience in stem cells, vascular biology, 3D organoid
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facilities, including advanced microscopy, controlled environment growth rooms, genomics, proteomics, and metabolomics platforms. • Opportunities to work across model and crop species, including Arabidopsis
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. Desirable Qualifications: Desirable Qualifications: Laboratory skills:in vitro and in vivo studies, flow cytometry, andtechniques in molecular biology Experience with mouse models. Competency in SolidWorks