Objectives of Programme's Learning Outcomes

• Scientific skills
• Implement and independently carry out an experimental approach, validate a model by comparing its predications with experimental results, assess the limitations of the model's validity, and identify sources or error.
• Use acquisition and data analysis software specific to biomedical sciences
• Be familiar with biomedical science research tools, including bioinformatics tools.
• Use basic mathematical tools
• Professional integration skills
• Invest their knowledge and skills in professional scenarios
• Network, and use digital communication and collaboration tools
• Work in teams in different contexts including with people from different disciplines, integrate, fit in, collaborate, communicate and report
• Work independently: - Get organised, manage time and priorities, assess themself. - Master assessment methods. - Continue learning individually, prepare to educate themselves throughout life. - Demonstrate abstract thinking. - Be introduced to project management. - Show initiative.
• Master written and oral scientific expression. Be independent in writing and show that they can communicate their thoughts, reason and organise their knowledge
• Master scientific English: - Present scientific results, orally and in writing. - Answer scientific questions in English

Learning Outcomes of UE

Not applicable

UE Content: description and pedagogical relevance

You will learn the foundations of Computational Neurosciences through these differets chapters
1 Why do we need data?
2 Bottom-up vs Top-down
3 Correlation versus causality
4 Filtering
5 Fitting
6 Prediction & perturbation
7 Model comparison principles

Prior Experience

Not applicable