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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.

Combining Partial Differential Equations, Machine Learning and Measurements for Increased Prediction Capability

The “Combining Partial Differential Equations, Machine Learning and Measurements for Increased Prediction Capability” graduate course in Linköping is open for applications.

Call for graduate course in Machine Learning or/and Artificial Intelligence

Call for graduate course in Machine Learning or/and Artificial Intelligence

SeSE is a Swedish initiative for PhD level education in the field of e-Science. SeSE was founded and is supported by the Swedish e-Science Research Centre (SeRC) and the Swedish e-Science Collaboration (eSSENCE). SeSE offers education in the form of academic courses for PhD students in fields where the use of e-Science is emerging and also e-Science education for students in fields which are already computing-intensive.

The graduate school has identified large interest in the area of Artificial Intelligence and Machine Learning and is calling for developing, or offering an already existing, advanced level course in format of the school in the Autumn term of 2019 or in the following year of 2020.  

Course format

  • The course material should correspond, but is not strictly limited, to an equivalent of 3 hp (högskolepoäng) at University PhD level.
  • Typical format of a course at the graduate school consists of
    • one week of preparatory work e.g., literature studies, carried out at the university where the student is enrolled (home university)
    • one week on campus with lectures and exercises (where the course is offered)
    • one week of work after the course is completed, typically project work and examination

For more details on the activities organized by the graduate school, mission and organization, please visit the SeSE web page at http://sese.nu/

You expression of interest application should contain a brief overview of the course topics, target audience, name and affiliation of the teacher(s) and a 2-page CV of the course responsible teacher.

You can submit your express your interest by emailing the SeSE coordination team before 30.09.2019. Please contact us if you need more information.

Contact information:

Pavlin Mitev, Director and administration
e-mail: pmitev@kemi.uu.se

Arvind Kumar, Vice director and coordinator
e-mail: arvkumar@kth.se

SeSE-course-call-ML_AI.pdf

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