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* After the course, the student is able to acquaint her/himself with the application of an experimental technique and can quantitatively argue the suitability of this technique to a given physical problem.
* After the course, the student has a basic knowledge of data analysis/presentation techniques relevant to a given field of research. The student can implement these techniques using a programming language and libraries relevant to that field.
* After the course, the student knows how to collect, manage and interpret data in types/formats relevant to their specific field of research.
* After the course, the student can discuss, describe and interpret data, both verbally and in the form of a short report.
* After the course, students can collaborate in a team to design and carry out a short experimental project.
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1. Significant and recent experimental breakthroughs
* Laser cooling and laser spectroscopy
* Scanning probe microscopy
* Electron microscopy
* Particle accelerators
* Ultrashort optical pulses
2. Open and reproducible science
* FAIR data principles
* scientific writing
3. Team science and project management
* team formation
* git workflow
And a selection of topics from this list based on the covered projects:
4. Hardware
* Vacuum technology
* Cryogenic technology
* Optical techniques:
* Interferometry
* Spectroscopy
* Holography
* Light sources
* Light detection and Cameras
* Magentic sensors
5. Interfacing instruments
* digitisation
* filters
* interfacing protocols
* programming workflow
* (electronic) feedback
6. Data processing
* signal processing
* statistical analysis
* noise
Other necessary topics will be chosen in discussion with the students.
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