computational modelling

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Lecturer: Dr. Lorenz Adlung, I. Department of Medicine, Hamburg Center for Translational Immunology (HCTI), bAIome, UKE

Prerequisites: Intrinsic motivation to learn about infection and inflammation using your computer.

Description: This workshop is open to all students, researchers and clinicians who want to learn how we use (“big”?) data and computational modelling for discovery and rational intervention in infection and inflammation. In today’s biomedical research, the bottleneck has shifted, and for the first time, data generation is no longer the rate-limiting step in scientific progress, but rather: data analysis. We will discuss current trends and show how we can use mathematical concepts and analytical thinking to address unmet clinical needs in influenza infection and inflammatory bowel disease. The workshop will be in presence and therefore each participant should bring their own laptop or ipad.

Topics

• “big” data and code repositories
• mathematical concepts relevant to infection and inflammation
• computational modelling of murine influenza infection
• identification of treatment responders in inflammatory bowel disease
• discussion of the future of bAIomedical research

learn_bAIome offers workshops and trainings in biomedical AI/data science with tailored formats that take into account background, programming skills and intensity to provide unique, focused, and effective courses. These courses are free and open to students, clinicians, and researchers across academic institutions in Hamburg.

Prerequisites: Intermediate level computational background and basic knowledge of machine learning

Description: This 3-day international workshop is organised by University of Hamburg’s European University Alliance for Global Health (EUGLOH), Hub of Computing and Data Science (HCDS) and Center for Biomedical AI at UKE (bAIome) to foster international exchange and cooperation among students and researchers working in machine learning relating to biomedical questions. The vision is to create a supportive network and inspire international collaborations.

The workshop will explore various aspects of machine learning using biomedical data with the hands-on practical projects providing the main focus, allowing participants to work in a team environment to understand how machine learning is applied to specific biomedical challenges.

For further details and registration check out the EUGLOH website