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Introduction to Scientific Computing

The “Introduction to Scientific Computing”  graduate course, previously given in Lund, will be held this year in Linköping and is now open for applications.

Welcome to the Workshop: “Deep Neural Networks for Beginners!

The workshop will be held from 07-09.10.2019 at Department of Meteorology, Stockholm University, with an invited guest-lecturer, Prof. Ribana Roscher,  Institute of Computer Science, University of Osnabrueck, Germany, who is an expert at applications of neural networks in the domain of Natural and Earth Sciences.
The need for methods to automatically, objectively, and efficiently analyze and interpret data is a common task in many scientific areas. Advances in the field of deep learning have led to a development in machine learning methods such as deep neural networks, which outperform classical data analysis methods in many fields. One of the main reasons of their success is the ability to uncover hidden and complex structures in the data, where layered architectures are employed to extract a deep and rich hierarchical feature representation. Several applied scientific communities such as the remote sensing community started to use deep learning approaches for their application tasks including the identification of objects and forecasting of bio- and geophysical parameters. This illustrates an increasing demand for interdisciplinary approaches that bridge the gap between machine learning and disciplines such as natural sciences. The global scope of this course is to lay the foundations in machine learning and provide necessary deep learning tools in the context of applied sciences. In detail, it includes lectures about fundamental and advanced concepts in neural networks and deep learning, which will be presented with allocated time for discussions. The gained knowledge will be applied in three hands-on sessions covering various practical aspects. The hands-on sessions will cover all necessary aspects of machine learning pipelines that work on real world applications, covering data pre-processing, model learning and testing, as well as quantitative and qualitative evaluation.
For more information on the content, schedule and how to apply for the course, please visit the course web page at: http://sese.nu/deep-neural-networks-for-beginners-2019/
Inga Monika Koszalka – Course Coordinator
Associate Professor of Coastal Oceanography
Department of Meteorology, Stockholm University (MISU)
SE-10691 Stockholm, Sweden
Tel: +46-8-161734
Fax: +46-8-157185
Email: inga.koszalka@misu.su.se

Computational Python

The “Computational Python” graduate course in Stockholm is open for applications.

Introduction to High Performance Computing

The “Introduction to High Performance Computing – PDC Summer School” graduate course in Stockholm is open for applications.

Advanced Molecular Dynamics

The “Advanced Molecular Dynamics” graduate course in Stockholm is open for applications.

Introduction to Climate modeling I & II

The “Introduction to Climate modeling I & II” graduate course in Stockholm is open for applications.

Introduction to Scientific Computing II

The “Introduction to Scientific Computing II” graduate course in Lund is now open for applications.

Scientific Visualisation course 2018

This year’s Scientific Visualisation course will be held from 26-th to 30-th of November in Uppsala,
with specially invited guest-lecturer, Alex Telea, who is the author of the course book “Data Visualization: Principles and Practice” on which the course is based itself.

The Scientific Visualisation course deals with methods that offer a way to see the unseen, how to select appropriate methods for a given data set, possibilities and limitations with methods, and how to use visualisation toolkits. During the course, you will learn to explore your data interactively in 3D or programmatically using ParaView – an open-source, multi-platform data analysis and visualisation application. While ParaView platform is developed for quick build of visualisations and data analysis using qualitative and quantitative techniques the tool is also well suited for analysis of extremely large datasets using distributed memory computing resources.
For more information on the content, schedule and how to apply for the course, please visit the course web page at http://sese.nu/scientific-visualisation-2018

Foundations of numerical optimization

The new graduate course “Foundations of numerical optimization” is open for applications.