This seminar focuses on the increasing importance of Artificial Intelligence (AI) in academic research and writing, providing practical insights into AI technologies; use in these areas.The workshop explores ChatGPT and prompt engineering, as well as other academic AI tools to aid research and writing, examining both benefits and challenges. Ethical aspects, such as copyright and authenticity of research results, are discussed, with the goal of equipping participants with practical knowledge and skills to effectively utilize AI in daily research through interactive elements like case studies and group discussions.

The course is practical and aims at teaching students how to:

• Use the programming environment R and RStudio, which includes installation, how to handle errors, problem solve and access helper documents.
• Use basic concepts of programming, such as data types, logical and arithmetic operators, if else conditions, loops and functions.
• Use common R packages to perform basic statistical analysis (e.g., t-test, chi2-test, correlation) and visual presentation (e.g., boxplot, histogram and heat-map) of data in R.

The course is structured with the intent to gradually make students more autonomous in writing code. Starting by introducing a concept through a lecture, then providing formative quizzes and tasks relateed to the concept. This all leads up to a project (exam) where the student gets to combine multiple concepts into a project with the intent of solving a certain problem or displaying specific statistical tests of visual components. 

Do you need to turn data into a publication figure? We offer tools and confidence for the student to independently select a statistical method for research questions in their field. The course is practical and includes implementing a basic statistical analysis in R, the leading statistical programming language in bioinformatics and medical science. Furthermore, we give a brief introduction to visualization in R, with a focus on R/ggplot2. Students can bring data from their own research project, or work on data from the course.

Dive into the cutting-edge world of nuclear medicine with this comprehensive course that blends theory and hands-on practice. In this one-week course, you will gain invaluable knowledge and skills at the forefront of medical imaging and targeted radiopharmaceutical therapies.

This course offers a unique opportunity to:

• Master the fundamentals of radiation physics and biology
• Explore state-of-the-art diagnostic and therapeutic applications in oncology and neurology
• Gain practical experience handling radiopharmaceuticals in a laboratory setting
• Understand the latest developments in personalised medicine using radioactive tracers

Upon completing the course, you will be allowed to handle radiopharmaceuticals and open radioactive sources at Karolinska Institute and Karolinska University Hospital.

The course will introduce neuropsychological assessment in an aging population, focusing on age-related cognitive changes and their neural correlates. An additional purpose is to increase understanding of cognitive aging and how to differentiate between non-pathological cognitive aging and early signs of pathology. After the course, you will be able to define and describe common neuropsychological concepts and measurement techniques and demonstrate an overall understanding of neuropsychological investigation methodology and cognitive diagnostics in aging. The course will give you an increased understanding of cognitive aging and the complexity of differentiating between “normal” and early-stage pathological aging.

The course is aimed for you who want to get an understanding of the principles of PET, the methodology used for neuroreceptor imaging and quantification, as well as to get an insight in important research ongoing in the field and in the clinical applications of PET.

Topics covered include:

• Coding: theory, practical training, coding styles, unit testing
• Collaborative software development workflows
• Data analytics workflows
• (Generalised) linear mixed effects models
• Bayesian statistics
• Data visualisation
• Workflow automation
• Meta-science