This intensive blended learning course equips PhD researchers with the entrepreneurial mindset, skills, and tools needed to transform scientific discoveries into viable innovations and ventures. The course combines online pre-work with a hands-on, 5-day lecture and workshop series (10-14th of November) and aims to give students and researchers an overview about entrepreneurship as well as common business tools and strategies, to be able to assess the commercialization potential of a scientific idea and to develop solutions towards market needs. Topics include:

• Bringing Scientific Inventions and Research Ideas to Market
• Entrepreneurship in all its Facets
• Designing for Demand / Lean Canvas
• Business Strategies and Models
• Effectuation and Design Thinking
• Intellectual Property
• Financing and Funding Strategies
• Ethics and Regulatory Frameworks
• Risk Management and Mitigation
• Networking, Collaboration and Pitching 
   

Through lectures, interactive workshops, peer collaboration, expert mentoring, and real-world case studies, researchers will learn how to identify opportunities, mobilize resources, and create value (financial, societal and/or cultural). Students will work in groups on three assignments: Lean Canvas, a report detailing Lean Canvas findings and assumptions and a slide deck for final presentations.

To register, contact:
Contact your local NTEU project manager

Instructors:
Ásgeir Jónsson – asgeirjo@ru.is
Hallur Þór Sigurðarson – hallursig@ru.is
Susanne Durst – susanned@ru.is

This course aims to equip students with a broad understanding of digital health, emphasizing not only technical skills but also ethical considerations and critical thinking when designing, developing, and implementing digital tools in healthcare settings. The main objective is to set the stage for digital health, in general, and to understand the impact of design, development, and use of digital tools within healthcare settings for optimization purposes, in particular. By the end of the course, the students will be able to illustrate introductory knowledge of digital health, encompassing design, development, and utilization of digital tools in healthcare settings, as demonstrated by their assignment, where the focus is to design a mobile application for a specific case as well as reflect on the ethical implications of working with artificial intelligence as an embedded part of healthcare.

The course instructor is Dr. Anna Sigridur Islind, a professor at the department of computer science at Reykjavik University. 

To register, contact: islind@ru.is

Our MATLAB-based comprehensive course is designed to equip you with the essential knowledge and practical skills to delve into biomedical image processing, specifically tailored for biological/medical and neuroimaging applications using MATLAB.

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.

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