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:

• Basic fluorescence microscopy
• Fluorescence lifetime imaging (FLIM), 2P excitation
• Fluorescence resonance energy transfer (FRET)
• Stochastic optical reconstruction microscopy (d-STORM)
• 3D-Electron microscopy, focused-ion beam (FIB) milling and scanning EM, specimen preparation and embedding.

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.

This course will focus on behavioural, psychological, neurobiological, and neuropsychological processes underlying the acquisition of new knowledge and its subsequent consolidation and retrieval in human animals. Where possible, attempts will be made to integrate these levels in a multidisciplinary framework. Additionally, the application of learning and memory paradigms in clinical and cognitive research will be discussed. 

This course will provide students with a thorough background in the newly emergent field of social cognitive neuroscience. A broad range of social phenomena will be examined at multiple levels (1) the social level including experience and behaviors (2) the cognitive level which deals with information processing systems and (3) the neural level which deals with brain/neuronal bases of the first two levels.