DISA

We are now offering the first PhD-courses of fall 2018 related to DISA, Python 7,5 credits. It’s also open for other potential PhD-students.

Course content

The first lectures will introduce the basics of Python programming, including different ways to run (e.g., Jupyter) and test programs. This part will also cover some of the standard modules, such as NumPy, Pandas, and MatPlotLib.

The rest of the course is structured around “How do you do X in Python,” where X is a topic such as Network Analysis, Text Mining, etc. Each topic will be covered by one or a few overview lectures that cover some of the essential algorithms in detail, how to implement them in Python, and which modules are available to use. The lectures will introduce some important computer science and computational ideas as well as programming best practices.

The course will also briefly cover how to use the DISA HPCC and how to run Python programs on multicore machines and a cluster of such machines.

After completing the course, the student should:

• Be able to design algorithms to solve problems within their research domain and implement these using Python
• Be able to reason about the performance of an algorithm and its implementation, as well as use various tools to optimize their implementation, including parallelization.
• Know how to use essential Python modules, such as NumPy, SciPy, Scikits, Pandas, etc., as well as key modules within the topics (Xs) that the course covers.

Be able to reason about the benefits and drawbacks of Python as well as how it compares to other programming languages/environments and be able to argue for when and when not to use it.

Prerequisite

A completed undergraduate program of at least 240 credits, including 60 credits at advanced level, or the equivalent. Some knowledge of programming and/or algorithms will be helpful.

Timetabe

The course will start on September 10th and finish by the end of October/beginning of November. The course will mainly have lectures (live and video), with meet ups every other week.

Registration

The registration needs to be finalized no later than August 31st. Register here.

If you have any questions please contact Morgan Ericsson.

The findings, experiences, and ideas that emerge from research have traditionally been utilized through academic publication and teaching programs. However, academic impact alone is no longer enough for a successful research career. With the growing emphasis in the research funding landscape on maximizing impact beyond academia, it is increasingly important that researchers reach wider society by embedding non-academic impact strategies in their projects, by working with a range of non-academic partners, and by using ever more innovative methods of dissemination and utilization. This course showcases a range of approaches researchers can employ to ensure that their research has impact and relevance beyond universities. It will also provide students with tools that will help them best communicate the value of their work to research funding agencies and potential investors.

The course is offered to PhD students in all disciplines from Linnaeus University and three other universities. The course consists of three mandatory seminars in the autumn of 2018 (Örebro, Östersund and Stockholm) provided by the Faculty of Humanities and Social Sciences, Karlstad University. The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

You can apply to the course from 1st March at 9:00 AM. Please send your application to fyrklovern.doktorand@kau.se by 11th May. You should provide your name, department, contact details, and a short description (max. 100 words) of your research project. Please ensure that you obtain your supervisor’s approval for attending the course, and also state their name in your application email.

For more information see course description or contact Mathias Gaunitz, Grants and innovation office at Linnaeus University

//Diana

We are now offering the second PhD-courses related to DISA. It’s also open for other potential PhD-students.

Course content:

Data mining and machine learning is an area within computer science with the goal of bringing meaning to and learning from data. This course mixes theory and practice, with a focus on applied machine learning where we learn what algorithms and approaches to apply on different types of data.

The course includes the following:

• Supervised learning, different types of data and data processing
• Algorithms for handling text documents
• Algorithms for handling data with numerical and categorical attributes
• Neural Networks
• Deep Learning for image recognition

Timetable

The course will start on Monday April 9 and finish by the end of the semester.

Registration

The registration needs to be finalized no later than March 19th 2018

Register here

If you have any questions please turn to Johan Hagelbäck  

//Diana

We are now offering one of the first PhD-courses related to DISA. It’s also open for other potential PhD-students.

Course content:

Information Visualization (InfoVis) is an area of research that focuses on the use of visualization techniques to help people understand and analyze abstract data (such as tables or hierarchies).

The course includes visual representations, interaction techniques and visualization systems for:

• text and documents,
• network data (graphs),
• time series,
• software-related data,
• SoftVis, WebVis, BioMedVis, and GeoVis.

Also discussed topics of importance are collaborative and personal visualization, Visual Analytics, evaluation methods for systems/tools, and research challenges in InfoVis and Visual Analytics.

Timetable

The course will start on March 19 and finish on by the end of the semester.

See the full time schedule 

Registration

The registration needs to be finalized no later than March 2nd 2018

Register here

We will soon be launching more courses – so keep an eye out!

//Diana