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Advanced Instrumentation, Automation, and Data Handling in Experimental Science
Provider: Science

Activity no.: 5932-25-11-31There are 18 available seats 
Enrollment deadline: 15/08/2025
Date and time27.10.2025, at: 00:00 - 31.10.2025, at: 16:00
Regular seats20
ECTS credits2.50
Contact personStine Stenfatt West    E-mail address: west@nbi.ku.dk
Enrolment Handling/Course OrganiserJean-Baptiste Sylvain Béguin    E-mail address: jbeguin@nbi.ku.dk
Exam requirementsThe oral exam will be held in week 45, the week after the course.
Exam detailsActive participation in lectures and labs. Project presentation in a ‘conference’ attended by all participants at the end of the course. This also provides training for the PhD students for their first presentations at real conferences.
Grading scalePassed / Not passed
Aim and content
Progress in modern science and technology is based on advanced instrumentation, high-throughput data acquisition and processing, and automation. This ranges from development of quantum devices, nano-fabrication and characterization, quantum sensing over observational astronomy and ice core analysis.
The course offers a solid introduction to instrumentation, data handling and automation methods with use cases inspired by state-of-the-art research conducted at NBI. It is highly relevant for PhD students working on advanced experiments in natural sciences.
The course is organised in lectures/tutorials, hands-on projects and a final presentation session on the project work.

Formal requirements
The student should hold a Masters’ degree in natural sciences or engineering. A strong interest in experimental work and modern advanced scientific instrumentation is recommended.

Learning outcome
Knowledge:
• Optimal structures of scientific data bases
• Working principles of SEM and image mechanisms of SE and BSE
• Transfer function of optical cavities and Mach-Zehnder interferometers
• Electro-optic signal conditioning and servo loops
• Operational principles and characteristics of single-photon detectors and time-taggers
• Characterisation methods for single-photon sources

Skills:
• Write computer programs for advanced instrumentation and automatization
• Understand and set up Pound-Drever-Hall techniques
• Describe PID controllers for active stabilization
• Discuss limitations of instruments
• Understand and set up a Hanbury-Brown-Twiss experiment
• Process large time-tag datasets to extract photon characteristics
• Know how to take automation images on SEM, use Python script to process SEM images
• Make scientific decisions based on data analysis

Competences:
This course gives the student a solid experience with modern instruments, practical automation and control methods while developing important scientific data handling skills. It provides students with a good basis for laboratory work in PhD projects and experimental work in natural sciences.

Target group
This course is designed for PhD students in the natural sciences who are engaged in experimental research and aim to enhance their skills in instrumentation, automation, and scientific data handling. It is particularly relevant for students who want hands-on experience with state-of-the-art equipment, measurement techniques, and feedback control methods to automate data collection in advanced setups for e.g., quantum devices, nanofabrication, laser spectroscopy, and microscopy. The course targets students seeking to develop practical skills that will support laboratory work in their PhD projects and enable efficient, automated data-driven experimentation.

Teaching and learning methods
The course consists of lectures/tutorials and hands-on project work. The lectures will introduce key concepts and common elements in experimental methods that the students will practice during their project work.
The course material will include a project catalogue that covers specific applications in, e.g.:
• Modern quantum optics and nanophotonics setups
• Experiments with superconducting quantum devices
• Scanning electron microscopy characterization
• Advanced nanofabrication techniques

Lecturers
Jean-Baptiste Sylvain Béguin
Kasper Grove-Rasmussen

Workload
Preparation: 25h+5h (conference prep.)
Lectures: 10h+3.75h (conference)
Laboratory: 25h


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