PhD-course

PhD course INNOVATIVE APPLICATIONS OF RESEARCH AND SCIENCE (4.5 credits)

Friday, March 6th, 2020

The findings, experiences, and ideas that emerge from research have traditionally been utilized through academic publication and teaching programmes. 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 funders and potential investors.

How to Apply?
Opens 2nd 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.

Eligibility and further details
The course is offered to PhD students in all disciplines from Karlstad University, Linnaeus University, Mid Sweden University and Örebro University. The course consists of three mandatory sessions in the autumnof 2020 (at Karlstad, Sundsvall, and Stockholm). The course syllabus has been evaluated and approved by the Research Education Committee at Karlstad University’s Faculty of Humanities and Social Sciences. The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

The syllabus at https://libra.sae.kau.se/course/att-nyttiggora-forskningoch-vetenskapinnovative-applications-research-andscience

For more information contact: Martina Lago, Innovation Advisor, martina.lago@lnu.se or 0480-446062
PhD-course 2020

New PhD-course in Statistical Learning 7,5 credits

Tuesday, January 21st, 2020

The main objective with this course is to get an introduction into modern statistical methods for modeling and and prediction of data. After successfully completing the course, the student is anticipated to be able to

  • Demonstrate a conceptual understanding of the following fields in statistics: classification, resampling methods, linear model selection and regularization, and unsupervised learning.
  • Apply modern statistical software for classification, resampling methods, linear model selection and regularization, and unsupervised learning.

The course contains

  • Linear regression: simple and multiple linear regression with assessing the accuracy of the coefficients and the model and comparison with K-nearest neighbors
  • Classification: logistic regression, linear discriminant analysis, K-nearest neighbors
  • Resampling methods: cross-validation, bootstrap
  • Linear model selection and regularization: subset selection, shrinkage methods, dimension reduction methods, considerations in high dimensions
  • Unsupervised learning: Principal component analysis, clustering methods
  • Writing and presentation of a report where real data materials are analyzed with appropriate statistical approaches from the particular statistical field

Type of Instruction
Teaching consists of lectures, presentations, laboratory work, and tutoring.

Examination
The course is assessed with the grades A, B, C, D, E, Fx or F. The grade A constitutes the highest grade on the scale and the remaining grades follow in descending order where the grade E is the lowest grade on the scale that will result in a pass. The grade F means that the student’s performance is assessed as fail (i.e. received the grade F). The student’s knowledge is assessed in form of

  • Graded conceptual assignments (3 credits), grades A to F
  • Graded computer assignments (3 credits), grades A to F
  • Presentation of the use of statistics in the student’s research alternatively presentation of a statistical topic not covered in this course (1,5 credits), grading scale U-G.

Required reading
G. James, D. Witten, T. Hastie, R. Tibshirani, An introduction to statistical learning: with applications in R, latest edition, Springer.
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer, latest edition.

Timetable
The course will start on Fri March 27 and finish by the end of May/beginning of June. If convenient for participants it is suggested that we meet weekly on Fridays at, say 13:15.

Registration
Try to finalize the registration no later than Mar 17 so it will be easier to plan. Register here https://forms.gle/a3zzgG5toouMFqYb7

Prerequisites
1MA501 Probability Theory and Statistics 7,5 credits or an equivalent course in mathematics, mathematical statistics, or statistics.

If you have any questions please contact Roger Pettersson (https://lnu.se/personal/roger.pettersson/).

New course: Digital Humanities Research Methods (7.5 credits)

Tuesday, October 8th, 2019

The course “Digital Humanities Research Methods” is given at Linnaeus University, Sweden, online, in English, from 30 March 2020 till 03 May 2020, and is free of charge for EU citizens. 

The aim of this course is to learn about digital research methods to address research questions from the humanities. The course gives an overview of the impact of digitization on the way research is conducted, an insight into a range of different digital methods, as well as an awareness of difficulties related to the methodology. The deadline to apply is 15 October.

For more information about the course and how to apply see: https://lnu.se/en/course/digital-humanities-research-methods/distance-international-autumn/

PhD-course: Applied Machine Learning 3 credits

Thursday, August 29th, 2019

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

The course will start on Tuesday October 8th with workshops on October 29th, November 26th
The registration needs to be finalized no later than September 19th 2018 via this link https://forms.gle/Qgk91hk7tTxzp7rs7

If you have any questions please turn to Johan Hagelbäck – johan.hagelback@lnu.se

PhD course INNOVATIVE APPLICATIONS OF RESEARCH AND SCIENCE (4.5 credits)

Monday, April 29th, 2019

The findings, experiences, and ideas that emerge from research have traditionally been utilised through academic publication and teaching programmes. However, academic impact alone is no longer enough for a successful research career. With the growing emphasis in the research funding landscape on maximising 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 utilisation. 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 funders and potential investors.

Lecturers and content

You will be provided with knowledge around idea development within research. Particular emphasis will be given to developing an understanding of ’research impact’ and how to embed this in your projects.

Eligibility and further details

The course is offered to PhD students in all disciplines from Karlstad University, Linnaeus University, Mid Sweden University and Örebro University. The course consists of three mandatory seminars in the autumn of 2019 (Karlstad Sep 11-12th, Kalmar Oct 22-24th and Stockholm Nov 21st). The innovation office Fyrklövern covers the cost of your course travel and accommodation. The course is taught in English.

How to Apply?
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 please contact: Mathias Gaunitz – mathias.gaunitz@lnu.se or 0470-70 89 79

PhD course: eHealth – improved data to and from patients, 3 credits

Wednesday, January 2nd, 2019

In April 2019 we will give a new course for PhD-students in eHealth. The course will give an introduction to eHealth and health informatics including benefits and challenges with eHealth, examples of applications in use, register based epidemiology, decision support systems, overview and examples of research within the interdisciplinary field of health informatics.

Teaching in this course will be lectures online (via mymoodle) as well as 2 seminars where students will present and discuss papers from this field of research.

This course will be given in collaboration with  the eHealth Institute. We welcome PhD-students from DISA as well as other PhD-students at Linnaeus University who are interested in eHealth and health informatics.

  • Pace: Half time, distance learning with approximately 2 meetings on campus in Kalmar
  • Language: English
  • When: April 2019 (preliminary 1/4 – 28/4)
  • Contact: If you are interested in this course, please send an e-mail to Tora Hammar, tora.hammar@lnu.se

The eHealth Institute, Department of medicine and optometry, Linnaeus University will be responsible for the course.

//Diana

New chance to take the PhD-course in Applied Machine learning 3 credits

Monday, August 20th, 2018

We are not offering you a second chance to take the PhD-course in Applied Machine Learning this fall.

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 Tuesday October 9th and finish by the end of the semester.

Registration

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

Register here: https://goo.gl/forms/jn1DAAQsb5zm8S1D3

 

If you have any questions please turn to Johan Hagelbäck – johan.hagelback@lnu.se

First PhD-course in Python (7,5 credits) available for sign up

Wednesday, June 20th, 2018

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.

PhD course INNOVATIVE APPLICATIONS OF RESEARCH AND SCIENCE (4.5 credits)

Wednesday, March 28th, 2018

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