deploy: 2ed77a9

_sources/intro.md

@@ -4,6 +4,18 @@ This [Jupyter book](https://jupyterbook.org/) contains training resources for sc
 
 Here, we cover the *GPU-Accelerated Image Analysis* Track of the [PoL Bio-Image Analysis Symposium](https://biopol-training.eventmember.de/). We will process images using [clesperanto](https://clesperanto.github.io/), [cupy](https://cupy.dev/) and [Pytorch](https://pytorch.org/). We will denoise, deconvole and segment images with and without deep learning techniques. We will get an introduction in how to [TU Dresden HPC cluster Taurus](https://tu-dresden.de/zih/hochleistungsrechnen/hpc) and distribute tasks using [dask](https://www.dask.org/).
 
+## Trainers
+
+* Stéphane Rigaud, Institut Pasteur Paris
+* Marvin Albert, Institut Pasteur Paris
+* Johannes Soltwedel, PoL TU Dresden
+* Peter Steinbach, Helmholtz AI
+* Sebastian Starke, Helmholtz AI
+* Neringa Jurenaite, ScaDS.AI, TU Dresden
+* Brian Northan, True North IA
+* Robert Haase, PoL TU Dresden
+
+
 ## Timetable
 
 ![img.png](timetable.png)

intro.html

@@ -467,6 +467,7 @@ <h2> Contents </h2>
             </div>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#trainers">Trainers</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#timetable">Timetable</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-use-this-material">How to use this material</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feedback-and-support">Feedback and support</a></li>
@@ -486,6 +487,19 @@ <h2> Contents </h2>
 <h1>PoL Bio-Image Analysis Training School on GPU-Accelerated Image Analysis<a class="headerlink" href="#pol-bio-image-analysis-training-school-on-gpu-accelerated-image-analysis" title="Permalink to this heading">#</a></h1>
 <p>This <a class="reference external" href="https://jupyterbook.org/">Jupyter book</a> contains training resources for scientists who want to dive into GPU-accelerated image processing. It specifically aims for students and scientists working with microscopy images in the life sciences.</p>
 <p>Here, we cover the <em>GPU-Accelerated Image Analysis</em> Track of the <a class="reference external" href="https://biopol-training.eventmember.de/">PoL Bio-Image Analysis Symposium</a>. We will process images using <a class="reference external" href="https://clesperanto.github.io/">clesperanto</a>, <a class="reference external" href="https://cupy.dev/">cupy</a> and <a class="reference external" href="https://pytorch.org/">Pytorch</a>. We will denoise, deconvole and segment images with and without deep learning techniques. We will get an introduction in how to <a class="reference external" href="https://tu-dresden.de/zih/hochleistungsrechnen/hpc">TU Dresden HPC cluster Taurus</a> and distribute tasks using <a class="reference external" href="https://www.dask.org/">dask</a>.</p>
+<section id="trainers">
+<h2>Trainers<a class="headerlink" href="#trainers" title="Permalink to this heading">#</a></h2>
+<ul class="simple">
+<li><p>Stéphane Rigaud, Institut Pasteur Paris</p></li>
+<li><p>Marvin Albert, Institut Pasteur Paris</p></li>
+<li><p>Johannes Soltwedel, PoL TU Dresden</p></li>
+<li><p>Peter Steinbach, Helmholtz AI</p></li>
+<li><p>Sebastian Starke, Helmholtz AI</p></li>
+<li><p>Neringa Jurenaite, <a class="reference external" href="http://ScaDS.AI">ScaDS.AI</a>, TU Dresden</p></li>
+<li><p>Brian Northan, True North IA</p></li>
+<li><p>Robert Haase, PoL TU Dresden</p></li>
+</ul>
+</section>
 <section id="timetable">
 <h2>Timetable<a class="headerlink" href="#timetable" title="Permalink to this heading">#</a></h2>
 <p><img alt="img.png" src="_images/timetable.png" /></p>
@@ -589,6 +603,7 @@ <h2>Acknowledgements<a class="headerlink" href="#acknowledgements" title="Permal
   </div>
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
+<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#trainers">Trainers</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#timetable">Timetable</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#how-to-use-this-material">How to use this material</a></li>
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feedback-and-support">Feedback and support</a></li>