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• • Content provider
  • NanoCommons1
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  • www.nanocommons.eu1
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  • EU H20201
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• • Target audience
  • Graduate students768
  • Postdocs and Staff members from the University of Cambridge489
  • Institutions and other external Institutions or individuals486
  • PhD students359
  • Academics293
  • Industry291
  • PhD283
  • Plant research174
  • Researchers78
  • Life Science Researchers40
  • Biologists35
  • bioinformaticians35
  • Master students18
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  • life scientists17
  • mixed audience15
  • PhD Students14
  • Graduate Students13
  • This is aimed at life scientists with little or no experience in machine learning and that are looking at implementing these approaches in their research.13
  • as well as other departmental training within the University of Cambridge (potentially under a different name) so participants who have attended statistics training elsewhere should check before applying.12
  • Biologists, Genomicists, Computer Scientists11
  • Existing R users who are not familiar with dplyr and ggplot211
  • Professors11
  • Technicians11
  • This course is included as part of several DTP and MPhil programmes11
  • Those with programming experience in other languages that want to know what R can offer them11
  • All postgraduates9
  • Bioinformaticians9
  • Bioinformaticians and wet-lab biologists who can program9
  • PhD candidates9
  • Wet-lab researchers and bioinformaticians 9
  • PhD candidate8
  • Undergraduate students8
  • data steward / data manager8
  • data stewards8
  • postdoctoral researchers8
  • Researchers who are applying or planning to apply image analysis in their research7
  • postdocs7
  • Post Docs6
  • Postgraduate students6
  • Senior scientist/ Principal investigator6
  • The course is aimed primarily at mid-career scientists – especially those whose formal education likely included statistics6
  • but who have not perhaps put this into practice since.6
  • data managers6
  • Clinicians5
  • It may be particularly useful for those who have attended other Facility Courses and now need to process their data on a Linux server. It will also benefit those who find themselves using their personal computers to run computationally demanding analysis/simulations and would like to learn how to adapt these to run on a HPC.5
  • Life sciences5
  • Postdoctoral Researchers5
  • Postdoctoral researchers5
  • The course is open to Graduate students5
  • This course is aimed at students and researchers of any background.5
  • Trainers5
  • We assume no prior knowledge of what a HPC is or how to use it.5
  • software developers, bioinformaticians5
  • Anyone intersted in GWAS and using the H3Africa genotyping chip4
  • Bioinformaticians and wet-lab biologists who can program in Perl, Python or R. 4
  • Biomedical researchers4
  • Clinical Scientists4
  • Molecular Biologists4
  • Novice users of HPC and anyone who expects to need to use HPC systems at some stage in their research4
  • Pathologists4
  • Research presentation4
  • Researchers who want to extract quantitative information from microscopy images4
  • This course is aimed at researchers with an interest in metabolomics and its applications4
  • This workshop is aimed at researchers interested in proteins4
  • This workshop is aimed at researchers who need to undertake sequence searching as part of their work4
  • Wet-lab Researchers4
  • beginner bioinformaticians4
  • data manager4
  • database managers4
  • network analysis4
  • or who need to search against several biological datasets to gain knowledge of a gene/gene set4
  • protein-protein interactions and related areas4
  • health professionals3
  • <span style="color:#FF0000">There is no fee charged for this event''<span style="color:#FF0000">.3
  • Anyone who wants to become a teacher / trainer or a better one3
  • Beginner3
  • Beginners3
  • Bench biologists3
  • Bioinformaticians and wet-lab biologists who can program in Python or R. 3
  • Biologists and bioinformaticians who are dealing with high-throughput gene expression data or other high-throughput data and would like to learn state-of-the-art methods for mining and analysing such data.3
  • Computational biologists3
  • Core Facility Managers3
  • Developers3
  • Early Career Researcher3
  • Evolutionary Biologists3
  • Institutions and other external Institutions or individuals.3
  • Intermediate3
  • Masters students3
  • Medical Device3
  • PhD Students or young researchers in molecular biology and/or genetics with little or no background in bioinformatics. 3
  • Post-Docs3
  • Scientists3
  • Students and researchers from life-sciences or biomedical backgrounds3
  • The course is aimed at <b>bench biologists and bioinformaticians</b> who need to analyse their own data against large biological datasets3
  • The module is suitable for researchers interested in gene expression analysis and visualisation3
  • The workshop is aimed to biologists or computer scientists with little or no previous knowledge of Cytoscape3
  • This course is aimed at advanced PhD students and post-doctoral researchers who are applying or planning to apply high throughput sequencing technologies in cancer research and wish to familiarise themselves with bioinformatics tools and data analysis methodologies specific to cancer data. Familiarity with the technology and biological use cases of high throughput sequencing is required, as is some experience with R/Bioconductor (basic understanding of the R syntax and ability to manipulate R objects) and the Unix/Linux operating system. 3
  • This introductory course is aimed at biologists with little or no experience in machine learning.3
  • This workshop is aimed at researchers who are either generating or integrating molecular interaction data in their research. This could be protein-protein interaction as well as protein-RNA3
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