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Target audience
- Postdocs and Staff members from the University of Cambridge489
- Institutions and other external Institutions or individuals486
- Graduate students485
- 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
- PhD students12
- 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
- Existing R users who are not familiar with dplyr and ggplot211
- 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
- Bioinformaticians and wet-lab biologists who can program8
- Researchers who are applying or planning to apply image analysis in their research7
- post-docs7
- Researchers6
- 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
- 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
- The course is open to Graduate students5
- This course is aimed at students and researchers of any background.5
- We assume no prior knowledge of what a HPC is or how to use it.5
- Novice users of HPC and anyone who expects to need to use HPC systems at some stage in their research4
- 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
- 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
- <span style="color:#FF0000">There is no fee charged for this event''<span style="color:#FF0000">.3
- Biologists3
- Institutions and other external Institutions or individuals.3
- PhD Students3
- Postdoctoral Researchers3
- 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 introductory course is aimed at biologists with little or no experience in machine learning.3
- This workshop is aimed at students on the Rare Diseases and Experimental Medicine MPhil courses at the University of Cambridge. Students from the wider clinical sciences group are also able to attend subject to space being available.3
- bioinformatics and other life scientists planning to work with next-generation sequencing data.3
- pathways and diseases.3
- wet-lab scientists3
- <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £575 </span style>2
- <span style="color:#0000FF">All Members of the University of Cambridge to pay £250 </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>2
- <span style="color:#FF0000">There is no fee charged for this event''<span style="color:#FF0000">2
- Anyone wanting to use OMERO to organize2
- Bench biologists2
- Biological sciences research students and postdocs who may want to use HPC in their research. Please note that Biochemistry first year graduate students book this course via their Moodle site not here.2
- Biologists and bioinformaticians2
- Cytoscape will have to be downloaded and installed on your device for the practical sessions.2
- Experimental Researchers2
- Facility Managers wanting to train users2
- Institutions and other external Institutions2
- Life Science Researchers2
- Principal Investigators2
- Principal Investigators from the University of Cambridge2
- Students and researchers from life-sciences or biomedical backgrounds2
- Suitable for students and early career researchers2
- The course is recommended for all staff and students who use Linux in their research or plan on attending the CI High Performance Computing facilities (Cluster) course.2
- The workshop is suitable for scientists that are producing sequence data and require a platform to publish it2
- This course is for researchers from a broad biological background as the techniques learned can be applied to any InterMine database2
- This tutorial is basic and requires no prior knowledge of any coding language or software.2
- This webinar is suitable for students and early career researchers with interest in Genomics2
- 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-RNA2
- This workshop is aimed at researchers who are looking for an overview of the bioinformatics resources provided by EMBL-EBI2
- This workshop is aimed at researchers who want to learn about pathways or identify pathways relevant to a set of molecules2
- This workshop is aimed at researchers who want to learn more about the UniProt resources2
- This workshop is aimed at researchers who wish to understand why data standards are important and how they can be used in practice2
- This workshop is aimed at researchers who wish to use and submit functional genomics data. This workshop will not however cover the analysis of such data.2
- This workshop is aimed at those who want to explore genetic variation data.2
- Undergraduate students2
- Wet-lab researchers and bioinformaticians 2
- and for bioinformaticians wishing to search and analyse third party sequence data.2
- and industrial investigations into diagnostic and pharmaceutical design across all human cancers.2
- annotate and publish imaging data2
- biological process or function.2
- cellular models of disease and computational techniques are used to identify and validate the causal links between targets2
- covering a diverse range of organisms and biological data.2
- data integration and visualisation2
- encompassing all aspects of academic research2
- or will shortly have2
- postdocs.2
- postdoctoral researchers2
- postgrad2
- protein-DNA and protein-small molecular interactions.2
- the need to apply the techniques presented during the course to biomedical data.2
- view2
- who are using or planning to use bioinformatics in their research2
- who have2
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee.1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Members of Industry to pay 575.00 GBP. </span style> <span style="color:#0000FF">All Members of the University of Cambridge1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £350. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £175. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £400. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £200. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- Affiliated Institutions and other academic participants from External Institutions and Charitable Organizations to pay 250.00 GBP. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>1
- All postgraduates1
- Any students, postdocs or RAs with an interest in HPC1
- Anybody interested in using Jupyter and OMERO1
- Applicants are expected to be at an early stage of using bioinformatics in their research with the need to develop their knowledge and skills further. No previous knowledge of programming is required for this course; group projects may give you the opportunity to learn basic programming, but participants will be supported in this by their mentors. Depending on your chosen project, an introductory programming tutorial may be given as homework prior to attending the course. Though programming skills are not a prerequisite for attending the course, we will ask participants to specify their current level of programming skills in the applications. This will allow the mentors to target the group projects better to the skills and needs of the final course participants. 1
- Applicants are expected to have an interest in learning about bioinformatics and/or are in the beginning stages of using bioinformatics in their research with the need to develop their skills and knowledge further.1
- Applicants should be researchers who are using large multi-omics datasets to infer systems biology models. This is an advanced-level course, and so we will select applicants who already have some experience (ideally 1-2 years) of working with systems biology modelling or related large-scale multi-omics data analysis. Additionally, applicants will be expected to have a working knowledge of using Linux commands, and experience of using a programming language (e.g. Python or Perl). 1
- Beginners who have very little background in using the command line and are enthusiastic to learn more1
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