74 multiple-sequence-alignment positions at University of Cambridge in United Kingdom
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wide range of student sponsors and Funding Councils, and the different working practices of multiple Doctoral Training Centres and Programmes. The Postgraduate Administrator is supported by the full-time
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generate and have access to clinical and immunological data as well as many large data sets, including spectral immune phenotyping, proteomics and single cell sequencing data (both spatial and droplet) and
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to communicate clearly, manage multiple tasks, and work both independently and collaboratively will be essential to your success in this role. We are looking for someone with: Previous experience in administration
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contribute to the smooth running of one of the world's leading universities. The Role: Capture and accurately record customer service requests and updates in the CAFM system using multiple communication
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extensive experience applying business analysis tools and techniques, including system integrations. You are a strategic thinker who aligns digital initiatives with institutional goals. You can harness data
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research group. The successful candidate will benefit from building upon novel datasets, exciting preliminary findings, and using unique experimental model systems. Techniques will include multiple imaging
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complex research projects involving multiple interdependent components. Experience in programming (python) and applying AI-assisted technologies to streamline research and analysis workflows is highly
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determined based on how your skills and experience align with the requirements of the role. Applicants are required to provide details of two referees. Typically, referees will not be contacted until an offer
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that align with our business needs. We welcome applications from individuals who wish to pursue a career change, regardless of IT-related work experience. We also welcome applicants from any age starting 16
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seeking access to housing and labour markets. Second, the research will broaden beyond ethnicity to include multiple dimensions of exclusion, such as gender, age, and disability. While spatial analysis and