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The Computing School will cover aspects that
are relevant to all fields of crystallographic computing. As the
main topic we will focus on the determination and the use of
eigenvectors and eigenvalues and their applications in the different
fields of crystallography. Applications range from the well known
principal component analysis and other statistical and machine
learning techniques to modelling using Markov processes, or
optimization of functions. Fields such as image processing,
information theory, and crystallography make use of these in many of
its most fundamental methods. It impacts directly macromolecular
crystallography in both
phasing and refinement aspects. In small molecule crystallography and materials science research, it allows the understanding of very fundamental physical properties of matter. In this school, our aim is to learn and teach about this general topic from the computing aspect of the fundamentals, but also the computing tools and the data. The topics will be delivered using lectures, practical sessions and open discussions. The materials will be covered by our speakers, but participants are invited to contribute with posters, tutorials and topics for discussion and hands-on programming exercises.
Active developers of crystallographic software,
tools and methods are invited to participate at this Computing
School. The school is not introductory and it is recommended that
participants already have some basic programming skills and
crystallographic knowledge. Our aim is to discuss and exchange ideas
and to tackle the challenges related to the method development in
crystallography. Participants can be early career scientists (PhD
students, Postdocs, etc.) as well as established developers.
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