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DTSTART;TZID=Europe/Stockholm:20230313T080000
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CREATED:20221212T141504Z
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SUMMARY:RNA-seq data analysis 
DESCRIPTION:This workshop will introduce the best practice bioinformatics methods for processing and analyses of RNA-seq data via a series of  lectures and computer practicals. \n\n\n\nImportant dates\n\n\n\nApplication open: December 15\, 2022  \n\n\n\nApplication deadline: February 12\, 2023 \n\n\n\nResponsible teacher/s\n\n\n\nJohan Reimegård\, Julie Lorent\, Nima Rafati \n\n\n\nContact information for questions regarding the course\n\n\n\nedu.rnaseq@nbis.se \n\n\n\nCourse fee\n\n\n\nA course fee* of 1800 SEK will be invoiced to accepted participants. This includes lunches\, coffee\, snacks\, and one course dinner. *Please note that NBIS cannot invoice individuals \n\n\n\nCourse content\n\n\n\nTopics covered will include: \n\n\n\n\nRNAseq quality control analysis \n\n\n\nMapping of short reads \n\n\n\nQuantification of gene expression \n\n\n\nExploratory data analysis \n\n\n\nDifferential gene expression \n\n\n\nGene set enrichment analysis\n\n\n\n\n\n\n\n\nMore details about the course can be found at: \n\n\n\nThe previous year website\n\n\n\n\n\n\n\nThe course will follow similar structure and topics. NOTE: In March 2023\, the course will be a 4-day-course on-site in Uppsala. \n\n\n\nEntry requirements\n\n\n\nRequired for being able to follow the course and complete the computer exercises: \n\n\n\n\nBasic knowledge in Linux and R \n\n\n\nYour own laptop with the following specs: \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAt least 6 GB of RAM. \n\n\n\n\n\nOperating system Win10\, OS X or Linux \n\n\n\n\n\nAt least 30 GB of free storage\n\n\n\n\n\n\nDesirable: \n\n\n\n\nExperience working with NGS data analysis or completed the NBIS workshop “Introduction to Bioinformatics using NGS data” \n\n\n\nExperience working in R or completed the NBIS course “R Programming Foundations for Life Scientists”\n\n\n\n\nDue to our best practice to have a high teacher to student ratio we have set the number of participants to a maximum of 20 participants. If we receive more applications\, participants will be selected based on several criteria. Selection criteria include correct entry requirements\, motivation to attend the course as well as gender and geographical balance. \n\n\n\n\n\n\n\nApplication
URL:https://www.scilifelab.se/event/rna-seq-data-analysis/
LOCATION:Navet\, SciLifeLab Uppsala\, SciLifeLab Uppsala\, BMC C11\, Husargatan 3\, Uppsala\, 75237\, Sweden
CATEGORIES:Course
ORGANIZER;CN="NBIS - National Bioinformatics Infrastructure Sweden":MAILTO:education@nbis.se
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DTSTART;TZID=Europe/Stockholm:20230320T090000
DTEND;TZID=Europe/Stockholm:20230324T170000
DTSTAMP:20260408T130832
CREATED:20221026T132922Z
LAST-MODIFIED:20230306T114543Z
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SUMMARY:NBIS workshop in Neural Nets and Deep Learning
DESCRIPTION:National course open for PhD students\, postdocs\, researchers and other employees in need of Neural networks and Deep Learning skills within all Swedish universities. \n\n\n\n\n\n\n\nImportant dates\n\n\n\nApplication opens: 2022-10-25 \n\n\n\nApplication closes: 2023-02-10 \n\n\n\nConfirmation to accepted students:  2023-02-17 \n\n\n\nResponsible teachers:  Claudio Mirabello\, Christophe Avenel \n\n\n\nIf you do not receive information according to the above dates please contact: edu.neural-nets-deep-learning@nbis.se \n\n\n\nCourse webpage\n\n\n\nApplication\n\n\n\n\n\n\n\nCourse fee\n\n\n\nA course fee* of 2200 SEK will be invoiced to accepted participants. This includes lunches\, coffee and snacks. \n\n\n\n*Please note that NBIS cannot invoice individuals \n\n\n\n\n\n\n\nCourse content\n\n\n\nThis course will give an introduction to the concept of Neural Networks (NN) and Deep Learning. \n\n\n\nTopics covered will include: \n\n\n\n\nNN building blocks\, including concepts such as neurons\, activation functions\, loss functions\, gradient descent and back-propagation\n\n\n\nConvolutional Neural Networks\n\n\n\nRecursive Neural Networks\n\n\n\nAutoencoders\n\n\n\nBest practices when designing NNs\n\n\n\n\n\n\n\n\nLearning outcomes\n\n\n\nUpon completion of this course\, you will be able to: \n\n\n\n\nDistinguish the concepts of “Artificial Intelligence”\, “Machine Learning”\, “Neural Networks”\, “Deep Learning”\n\n\n\nDistinguish between different types of learning (e.g. supervised\, unsupervised\, reinforcement) and recognise which applies to their own problem\n\n\n\nDistinguish between linear and non-linear approaches and recognise which is best suited for application to their own problem\n\n\n\nDescribe what a feed-forward neural network (FFNN) is\, along with its components (neurons\, layers\, weights\, bias\, activation functions\, cost functions)\n\n\n\nExplain how training of a FFNN works from a mathematical point of view (gradient descent\, learning rate\, backpropagation)\n\n\n\nExecute with pen and paper a few steps of training of a very simple FFNN model\n\n\n\nTell the difference between a shallow and a deep network\n\n\n\nExplain broadly how different NN architectures are wired and how they work\n\n\n\nImplement and apply the most appropriate architecture to a given problem/dataset\n\n\n\nAnalyze training curves and prediction outputs to evaluate if the training has been successful\n\n\n\nDebug possible issues with the training and suggest changes to fix them\n\n\n\nExplain the difference between training\, validation and testing\n\n\n\nDefine what overfitting is from a mathematical point of view\, and what issues it causes\n\n\n\nIdentify what constitutes good practices of dataset design and how to avoid introducing information leakage or other biases when building their own datasets\n\n\n\n\n\n\n\n\nEntry requirements\n\n\n\nRequired for being able to follow the course and complete the computer exercises: \n\n\n\n\nFamiliarity with Unix/Linux\n\n\n\nAbility to bring your own laptop with Python and Jupyter Notebooks  installed for the practical exercises\n\n\n\nProgramming/scripting experience in Python (e.g. having attended the NBIS workshop in basic Python or equivalent)\n\n\n\nBasic experience of statistics and mathematics (e.g. having attended the NBIS workshop Introduction to Biostatistics and Machine Learning or equivalent)\n\n\n\n\nDesirable: \n\n\n\n\nYou have experience of working with Jupyter Notebooks\n\n\n\nYou have a necessity  to work with large datasets (e.g. thousands of samples)\n\n\n\n\n\n\n\n\nDue to limited space the course can accommodate a maximum of 25 participants. If we receive more applications\, participants will be selected based on several criteria. Selection criteria include correct entry requirements\, motivation to attend the course as well as gender and geographical balance.
URL:https://www.scilifelab.se/event/nbis-workshop-in-neural-nets-and-deep-learning-2/
LOCATION:Navet\, SciLifeLab Uppsala\, SciLifeLab Uppsala\, BMC C11\, Husargatan 3\, Uppsala\, 75237\, Sweden
CATEGORIES:Course
ORGANIZER;CN="NBIS - National Bioinformatics Infrastructure Sweden":MAILTO:education@nbis.se
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DTSTART;TZID=Europe/Stockholm:20230327T090000
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SUMMARY:Regional Mass Spectrometry Imaging Spring Workshop
DESCRIPTION:The scientific program will aim to reflect cutting edge innovations and current developments in mass spectrometry imaging (MSI)\, alongside new applications in various areas. The goal for this workshop is to promote and educate academic\, industry and government scientists on the latest applications and innovations in MSI applied to biomedical\, biological and data science research areas. \n\n\n\nA three-day scientific agenda with oral presentations\, workshop discussions\, and poster presentations covering the latest developments and applications of MSI by leading and upcoming scientists from academic\, government\, and industry labs \n\n\n\n\n\n\n\nWebsite\, program & Registration\n\n\n\n\n\n\n\nImportant dates\n\n\n\nFebruary 10\, 2023              Deadline for regular fee and abstracts submission \n\n\n\n\n\n\n\nOrganizers & Contacts for questions\n\n\n\nPer Andrén\, Spatial Mass Spectrometry\, SciLifeLab\, per.andren@farmbio.uu.se \n\n\n\nJeanette Jansson\, Apotekarsocieteten\, jeanette.jansson@lakemedelsakademin.se
URL:https://www.scilifelab.se/event/regional-mass-spectrometry-imaging-spring-workshop/
LOCATION:Uppsala Konsert & Kongress\, Vaksala torg 1\, Uppsala\, 75331\, Sweden
CATEGORIES:Course
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