38 parallel-processing-bioinformatics Postdoctoral research jobs at KINGS COLLEGE LONDON in United Kingdom
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skills and proficient in the use of applications such as Microsoft Word, Excel, PowerPoint. Experience in preparing high-quality manuscripts and submitting them for publication in academic journals
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tissue samples from one of Europe’s largest and most well- characterised ILD cohorts. The postholder will be responsible for tissue processing ad generation of / sorting of primary lung cells from
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“Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process. * Please note that this is a PhD level role but candidates who have submitted
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at each stage of the recruitment process. We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community
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of what criteria will be assessed at each stage of the recruitment process. Further information: We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel
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contributions to work in climate change, disasters, smart cities, risk regulation, water, human migration and land surface processes related to for example agriculture, forests and landscape fire. We are also
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at the bottom of the next page after you click “Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process. * Please note that this is a PhD
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. This document will provide information of what criteria will be assessed at each stage of the recruitment process. * Please note that this is a PhD level role but candidates who have submitted their thesis and
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document will provide information of what criteria will be assessed at each stage of the recruitment process. Further Information We pride ourselves on being inclusive and welcoming. We embrace diversity and
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of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, general pre-trained transformers, prompt engineering, knowledge graphs, knowledge