Bringing life science professionals together and furthering skills in the scientific community are in SciLifeLab’s DNA. Our many events spanning a wide range of topics, present opportunities to develop your know-how and share your own experiences and network – sparking new ideas, as well as new collaborations. All events are free and open to everyone (unless otherwise stated), and we warmly welcome participants from industry, academia, healthcare, and beyond.
To support lifelong learning, the SciLifeLab Training Hub offers a vibrant community where instructors and learners come together to gain new skills and grow through shared knowledge. Explore all our courses, training, and educational offerings at training.scilifelab.se.
Welcome to the Integrated Structural Biology course, 2nd edition Organized by SciLifeLab in collaboration with PPS (Protein Production Sweden), ESS (European Spallation Source), MAX IV, ProLinC, NSC (National Supercomputer Centre), SwedNMR and InfraLife. September 2-13 in Stockholm, Linköping, Lund and Gothenburg Questions regarding the course? Send them to: cecilia.persson@nmr.gu.se. This course guides you through the different […]
National course open for PhD students, postdocs, researchers and other employees in all Swedish universities, in need of Epigenomics data analysis skills. We also welcome applications from outside of Sweden and from the non-academic sector, for more info contact us! Important dates and information Application opens: 2024-04-26 Application closes: 2024-08-28 Confirmation to accepted students: 2024-08-30 Course […]
A course from SciLifeLab Training Hub, NBIS, and VIB.
This course covers lectures and practical demonstrations in SEM and TEM techniques. The contents include principles of electron microscopy, specimen preparation, cryo-electron microscopy and correlative light-electron microscopy. The main focus will be on imaging biological samples, but the course may also be suitable for material scientists interested in high-resolution electron microscopy. Application deadline:10 September 2024 […]
The Advanced Light Microscopy Facility, ALM, at Scilifelab gives a two-week PhD-level course on four new imaging and fluorescence spectroscopy techniques: Depletion-based Super-Resolution Imaging MINFLUX Light-Sheet Imaging FCS-Methods Lecturers: Steven Edwards Erdinc Sezgin Ana Agostinho Francesca Pennacchietti Hans Blom Stefan Wennmalm Dates: Monday October 14th – Friday October 25th 2024 Credits: 3 hp for PhD […]
The aim of this workshop is to provide an integrated view of data-driven analysis of biological data through machine learning, graph and network analysis as well as constraint-based modeling integration methods.
The course is addressed to individuals with little or no experience in programming but who are enthusiastic about learning how to use R for data analysis and streamline their work.
This course provides a practical introduction to the writing of Python programs for the complete novice. Participants are led through the core aspects of Python illustrated by a series of example programs.
The workshop will introduce important research data management aspects through lectures, demonstrations, and hands-on computer exercises. The course is intended for researchers who want to take the first steps towards a more systematic and reproducible approach to analysing and managing research data.
This intense one-week workshop provides an introduction to the analysis of next generation sequencing data. Lectures on the theory of concepts will be paired with practical computational exercises in the Linux environment.
NBIS / ELIXIR-SE course open for PhD students, postdocs, group leaders and core facility staff at all Swedish universities interested in making their computational analysis more reproducible. Important dates and information Application opens: 2024-08-26 Application closes: 2024-10-18 Confirmation to accepted students: 2024-10-25 Course Leader and teachers: In case you miss information on any of the above […]
This course expands on common life science data analysis methods, including dimensionality reduction techniques beyond PCA, mixed-effects models for analysis of repeated measures, and survival analysis. We will also dive deeper into machine learning, covering more classification algorithms, ensemble techniques, optimization strategies and PLS methods for single and multi-omics data analysis