Take a sneak peek at EMBL’s courses and conferences for 2022

Download our 2022 preview poster

Following a year and a half of virtual events, many of you are probably looking forward to attending in-person courses and conferences. So are we! Based on the current trajectory of the COVID-19 pandemic, things are looking brighter for 2022 and we are making plans to return to providing you with onsite training and opportunities to meet and connect with each other as early in the year as possible. Naturally, we’ll have back-up plans in place should coronavirus pandemic disruption strike again, but for now most of our events next year are planned to take place face-to-face.

Our 2022 Course and Conference Programme is now live and features a large variety of exciting new scientific topics. Here are some of the highlights of the programme.

Download our 2022 poster here!
To see the full list of upcoming events, visit our events website.

Conferences

We begin the year with a virtual talent search conference that will allow the next generation of infection biologists to present their work and expertise to EMBL and a large number of participating institutes. This new format is especially interesting for postdoctoral fellows and young researchers working in infection biology.

In April, a symposium will shed light on the biological relationship between microbial infections and human cancer. While for many tumour viruses the causality is firmly established, the biological links for bacterial infections are still under research. This symposium will provide a stimulating platform for young scientists and students to present their research, network and develop further this new interdisciplinary field.

Another exciting and innovative topic will be addressed in “Phenotypic Plasticity Across Scales”, a meeting that focuses on the ability of organisms to adapt their form, physiology or behaviour to environmental cues and changes. The conference will highlight molecular mechanisms underlying plasticity and links to the environment. The meeting will also address the role of plasticity in driving evolutionary novelty and biological diversity.

Courses

In 2022 EMBL will also offer hands-on practical courses on the latest laboratory and computational technologies. Microscopy image analysis has become a key technology in research. The advanced EMBL virtual course on “Deep learning for Image Analysis” will teach the utilisation of neural networks to answer crucial biological questions.

Two courses will present methods and tools on how to integrate multi-omics data sets. The EMBL Course “Analysis and Integration of Transcriptome and Proteome Data” will teach wet-lab scientists the basics in data analysis and integration, while the advanced EMBO Practical Course on “Integrative Analysis of Multi-Omics Data” will equip computational scientists with state-of-the-art integration tools like multi-omics factor analysis.

Many of our hands-on practical courses address complete workflows from sample collection, through wet-lab experiments to computational data analysis. One of them is the EMBO Practical Course “Methods for Analysis of circRNAs: From Discovery to Function”. This course will teach cutting-edge methods to identify and study this class of non-coding RNAs.

On-demand training

Our open access bioinformatics training offerings are more popular than ever. Here you also have the option to learn at your own pace with our online tutorials and webinars to make sure you stay up-to-date with the latest scientific techniques!

If you’d like to keep up-to-date with the latest news from the EMBL Course and Conference Office, please sign up to our mailing list. You can also follow us on TwitterInstagramLinkedIn or Facebook.

 

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Meet the Trainer – Anna Kreshuk

PHOTO: EMBL/Marietta Schupp

Meet Dr. Anna Kreshuk, a group leader in the EMBL Cell Biology and Biophysics unit, whose group uses machine learning to develop automated methods to help biologists speed up image analysis. Anna joined EMBL in 2018 and has since been very active in building up training opportunities in her research field.

What is your research focus and why did you choose to become a scientist?

My research is concerned with developing new machine learning-based methods of the analysis of biological images. I enjoy doing science, both for the thrill of finding new things and the joy of seeing others do that in their domain with the help of our tools.

Where do you see this field heading in the future?

I hope to see most of the routine image analysis automated in the future. This will hopefully raise new research questions in biology which can only be answered by imaging at scale, creating, in its turn, more exciting research questions for us.

How has training influenced your career?

We develop software for end users without computational expertise, who want to solve biological problems we don’t quite understand. Participating in training has provided a lot of insight to the user side of things, brought new collaborations and even new research directions for me and for my group.

What is your number one tip for people looking for scientific training?

A one-week course can be a great start, however, it’s important to find out how you can get support with the new technology in your everyday work. Try to stay in contact with your course buddies, but also look for online communities. For image analysis, for example, there is a great forum connecting all the popular tools.

If you weren’t a scientist, what would you be?

My 7-year-old recently asked: “you say I can become anything I want to be, but then why didn’t you become an astronaut?”. Seriously though, I’d probably be a programmer, I love automating things.

You are organising the EMBL Course: Deep Learning for Image Analysis (20 – 24 January 2020). What is the greatest benefit of the course for the scientific community and what could the techniques in this course be used for in the bigger picture?

Deep learning has brought an enormous advance in computer vision. We can now analyse microscopy images in ways no one thought possible just 10 years ago. While the technology is getting more accessible every year, it’s still difficult even for computationally savvy biologists to apply state-of-the-art methods to their image analysis problems. This is exactly the gap we intend to close.

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