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

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.

The course content lean on Design thinking ideology and methodology that inform research as well as developmental work and form a practice-based foundation for innovation aimed to meet the needs of societal and scientific environments. As a student, you work  individually from your own research perspective, and get helpful insights for future research.  

The course apply a problem-oriented teaching and learning style,  with a pedagogy that enables students to take active responsibility for individual and also group learning in a multi-disciplinary context.

Progress in medical innovation stands as a catalyst for worldwide economic growth. Whether within pharmaceuticals, medical devices, biotechnology, information technology, or a fusion of these advancements, the potential advantages extend significantly to private businesses and societal well-being. However, due to long development times and rigorous regulations, these innovative concepts require a lot of financing to establish their presence in the market. Consequently, assessing the value of novel medical innovations remains crucial. The primary objective of this course is to empower researchers with the proficient capability to effectively assess the worth of emerging technologies or processes within the medical industry.

The course aim is that the doctoral student develops a theory of science approach by enabling the doctoral student to understand, employ, reflect upon and critically assess concepts and ideas of theories of science as well as their implications for in particular medical scientific practice. A further aim is to enable the doctoral student to understand, reflect upon and critically assess views on and implications of definitions of health and disease.

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