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

The purpose of this course is to enable doctoral students and other participants to gain an understanding of the major neuroinflammatory diseases and the key players involved, including the interaction between the central nervous and immune systems. An additional purpose is that those who participate in the course learn to understand critical aspects of creating and using experimental systems to model neuroinflammatory diseases.

The course is offered full time, Monday-Friday, 9:00-17:00 at the Center for Molecular Medicine (CMM) on Karolinska University Hospital, campus Solna, building L8, lecture hall and seminar rooms.

This course is given jointly by the doctoral programmes Allergy, immunology and inflammation (Aii) and Neuroscience (Neuro

Developmental biology lies at the heart of an effort to understanding complex biological systems. By studying how neural circuits are assembled we can extrapolate key aspects of their function as well as devise strategies for their repair. This course is given to deepen the understanding of how molecular and cellular mechanisms underlie neurobiological function and to widen the horizon of students within the strong Karolinska neuroscience community.

The course is given in collaboration with the Master's Programme in Biomedicine.

This is a full time course given in person at Biomedicum, Campus Solna.

Link to course evaluation

https://survey.ki.se/Report/5biVHpOK5wg

Experimental neuroscience is key to progress in the understanding of how the brain functions. The experimental toolbox for studies in rodents is currently without comparison, allowing detailed investigation of how the brain is built and the function of brain circuits. Technological advances also make it possible to directly connect neurons and circuits to behaviour. 

In the Brain Circuits course, students will meet international and KI neuroscientists who have made significant contributions to the study and understanding of neuronal circuits and behaviour. The development and application of novel technologies and analysis (high-density electrophysiology and imaging of single-neuron activity, optogenetics, behavioural tracking, machine learning etc) will be covered, with a focus on advances using transgenic rodents. We have a strong emphasis on engaging junior neuroscientists in the course and on creating a network for future neuroscience leaders.

This course is given in collaboration with the Master's Programme in Biomedicine.

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 workshop provides a hands-on learning experience with a focus on a wider variety of AI tools, their ethical implications and their practical applications. The aim is to facilitate the responsible and efficient use of AI-based tools in research and academia.

Content:

• Understand the importance of using AI in research and academia and assess the benefits and risks involved
• Craft effective prompts for your research tasks
• Develop strategies to integrate AI tools into your research workflow
• Stay informed about and adapt to new developments in the field of AI

At the end of the workshop, you will receive a list of generative AI prompts useful in research and academia. There will be practice sessions during the workshop for which you will need access to AI tools, particularly ChatGPT/GPT-4o. If you do not have an account with ChatGPT/GPT-4o, alternatives like Microsoft Copilot, Google Bard or Claude.ai could also be used.

In the workshop “Fit for AI - Prompting for advanced users” you can get to know and try out prompting tips and techniques. You will learn about prompting techniques such as Few Shot Prompting, Chain-of-Thought Prompting and others. You can try out these techniques directly on various tasks and your own examples.

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 is designed to provide students and researchers with a solid understanding of functional Near-Infrared Spectroscopy (fNIRS) as a relatively new tool to measure brain activity and will emphasize both theoretical knowledge and practical skills of fNIRS. The students will gain expertise in the underlying principles of fNIRS, its instrumentation, and various analytical approaches. The primary goal is to empower students with the knowledge of this additional neuroimaging tool to design and execute advanced experiments, interpret fNIRS data effectively, and contribute to cutting-edge research in neuroscience and related fields.

The main purpose of the course is to provide the students with a solid understanding of the tools available to analyze brain structural data measured with structural magnetic resonance imaging (sMRI). The students will develop the ability to critically review results provided by different methods, to select the most adequate tools and experimental designs to answer different questions and to compare their relative advantages.

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.

The course covers the theoretical background to the brain imaging methods sMRI, fMRI, PET, EEG and MEG, such as what aspects of the human brain's structure and function they register, and the operation principles of the imaging instruments. The coursed gives the student a good understanding in how the different methods are used within in academic research as well as within health care. The course also addresses how the imaging methods can be combined in multimodal analyses, and discusses the interplay between development of theory, instrumentation, method, and applications.
The course begins with an introduction to brain imaging methods within neuroscience. In separate course modules, the course then offers the student a deeper understanding of the different methods sMRI, fMRI, PET, EEG and MEG, as well as combining them in multimodal brain imaging. Finally, the students will deepen their knowledge on a topic of their choice in an individual study project.

The purpose of the course is to introduce the topic of artificial intelligence (AI) in mental healthcare focused on theoretical development, ethics and practical application informed by a scientific approach.

This course will elevate your AI proficiency, preparing you to actively engage in the digital evolution of healthcare. It offers a comprehensive perspective on the healthcare shift, steered by medical necessities, and bolstered by innovative artificial intelligence solutions.

Topics covered include:

• Computational design strategies
• Differential equations
• Programming in Python
• Data analysis

Topics covered include:

• Coding: basic concepts, practical training, testing
• Foundations of sensor technologies
• Foundations of Bluetooth communication
• Usage of advanced programming interfaces (APIs)
• Analysis of time series data
• Introduction to machine learning techniques

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

Topics covered include:

• Reconstruction of neuron morphologies
• Histological preparation of brain tissue
• Electrophysiological recordings of single neurons in vivo
• Simulations of cellular function via multi-compartmental neuron models

This course covers acquisition and advanced analysis of MRI-data in clinical research including scanning
routines, tractography, tract-based spatial statistics, voxel-based morphometry and
support machine vector programming.

Topics covered include:

• Electrophysiological recording techniques
• Design of cognitive paradigms
• Spike detection and spike sorting
• Peri-stimulus time histograms
• Data analysis and statistical evaluation

Topics covered include:

• Basics of MRI and functional MRI
• Design of psychological experiments
• Analysis of functional MRI data
• Functional Neuroanatomy

Topics covered include:

• linear and nonlinear time series analysis methods for the characterization of complex dynamical systems
• statistical tools
• analysis of biomedical data (e.g. EEG, structural/functional MRI data)

This course will cover:

• Intro to Jupyter Notebooks, IDEs
• Intro Python (loops, variables, functions)
• Core packages (Numpy, Pandas, Matplotlib, Seaborn)
• Accessing folders (shell, OS)

The module presents a variety of fundamental models and methods from computational neuroscience. By solving daily exercises the students learn how to practically apply the acquired concepts. The course introduces the employed more
advanced mathematical tools embedded into the different topics. Further there will be a pre-course teaching the required programming skills in python.