Advanced Scientific Programming in Python
an Autumn School by the G-Node, the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler
Trento, October 4 - 8, 2010
Organizers: Paolo Avesani, Zbigniew Jedrzejewscy-Szmek, Tiziano Zito
Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game.
We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented.
This school is targeted at Post-docs and PhD students from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. A basic knowledge of the Python language is assumed. Participants without prior experience with Python should work through the proposed introductory materials.
2nd G-Node Winter Course in Neural Data Analysis
Organizer: Sonja Grün (RIKEN BSI, Japan)
The German Neuroinformatics Node (G-Node) organizes its second training course to promote state-of-the-art methods of neural data analysis among PhD students and postdocs. During 4 days the course offers hands-on experience with model-driven analysis of data from intra- and extracellular electrophysiology. We encourage applications from students/postdocs with an experimental background that want to widen their repertoire of analysis methods as well as from students with a theoretical background that have developed an interest analyzing physiological data in order to test model predictions.
Advanced Scientific Programming in Python
a Winter School by the G-Node and University of Warsaw
Warsaw, February 8 - 12, 2010
Organizers: Piotr Durka, Joanna & Zbigniew Jedrzejewscy-Szmek, Tiziano Zito
Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game.
We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented.
This winter school is targeted at Post-docs and PhD students from all areas. Substantial proficiency in Python or in another language (e.g. Java, C/C++, MATLAB, Mathematica) is absolutely required. An optional, one-day introduction to Python is offered to participants without prior experience with the language.
Advanced Scientific Programming in Python
Berlin, August 31 - September 4, 2009
Organizers: Michael Schmuker and Tiziano Zito
Many scientists spend much of their time writing, debugging, and maintaining software. But while techniques for doing this efficiently have been developed, only few scientists actually use them. As a result, they spend far too much time writing deficient code and reinventing the wheel instead of doing research. In this course we present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of the programming scientist. To spice up theory and foster our new skills in a real-world programming project, we will team up to develop an entertaining scientific computer game.
We will use the Python programming language for the entire course. With a large collection of open-source scientific modules and all features of a full-fledged programming language, Python is rapidly gaining popularity in the neuroscience community. It enables the scientist to quickly develop powerful, efficient, and structured software and is becoming an essential tool for scientific computing.
The summer school is targeted at Post-docs and PhD students from all areas of neuroscience. Substantial proficiency in Python or in another language (e.g. Java, C/C++, MATLAB, Mathematica) is absolutely required. An optional, one-day pre-course is offered to participants without Python experience to familiarize with the language.
Have a look at the Wiki
Image Processing School
Pilsen, September 9 - 12, 2009
Organizer: Albert Cardona
A sattelite to the 2nd INCF Congress of Neuroinformatics in Pilsen, this course, sponsored jointly by the Swiss, German, and UK National Nodes, offers training in image processing using open source tools, addressing fundamentals of image processing, image analysis, image registration, image segmentation and 3D modeling.
Visualization and Segmentation Methods in Neuroscience
Berlin, March 11 - 13, 2009
Organizer: Jürgen Rybak
A G-Node Course in Visualization, Reconstruction and Segmentation of neuro-morphological data with particular emphasis on image stacks acquired by confocal microscopy. Beginners and advanced users are welcome. Seminar and practical examples for training are provided but participants are also invited to bring their own data.
1st G-Node Winter Course in Neural Data Analysis
Munich, Jan 26-30, 2009
Organizer: Martin Nawrot (Freie Universität Berlin)
The G-Node Winter Course in Neural Data Analysis is planned on a yearly basis with. This 1st course gives hands-on experience with neural data analysis for PhD students and young postdocs. Participants with either theoretical or experimental background are equally invited.