Skriv ut Lukk vindu


 

Høst 2016

FYS-8029 Optical nanoscopy - 10 stp


The course is administrated by

Institutt for fysikk og teknologi

Type of course

The course is available as a singular course.

The course will only be taught if there is a sufficient number of students. If you are interested in following the course, please contact the student advisor as soon as possible


Course overlap

FYS-3029 Optical nanoscopy 8 stp

Course contents

The objective of this course is to provide education and training on state-of-the-art optical super-resolution imaging techniques. The emerging field of super-resolution optical microscopy is commonly referred to as optical nanoscopy. The focus will be on structured illumination microscopy (SIM) and direct stochastic optical reconstruction microscopy (dSTORM) techniques. The course will provide theoretical and practical training on the state-of-the art optical nanoscopy techniques. The teaching will be given as lectures, demonstrations, and practical sessions. Students will be provided opportunities to image their own sample (within limitations) using state-of-the-art SIM and dSTORM microscopes to be installed at UiT. Optical nanoscopy has had an impact in several disciplines. Similarly, this course is planned to be cross-disciplinary and it is suitable for candidates with different backgrounds, such as physics, biology, medicine, fisheries, pharmacy, who are interested to learn or use the emerging field of optical nanoscopy. The course is open to both internal and external students. The contents of the course are:

The course will provide brief overview on image formation and microscopy before venturing into the arena of super-resolution optical microscopy. The course will be given at both Master and PhD level. For Master level, cell staining, image acquisition, 3-D data rendering will not be mandatory and practical hands-on training will be less extensive. Practical training on SIM and dSTORM will be more extensive at PhD levels.


Application deadline

Registration deadline for PhD students at UiT - The Arctic University of Norway: September 1st

Application deadline for external applicants: June 1st


Admission requirements

To take PhD courses you need to have at least a master's degree or equivalent.

PhD students at UiT The Arctic University of Norway register for the course through StudentWeb .

External applicants apply for admission through SøknadsWeb.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

More information regarding PhD courses at the Faculty of Science and Technology is found here.


Language of instruction

The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English, but may be answered either in English or a Scandinavian language.

Teaching methods

Lectures: 20 hours Exercises: 30 hours

The course will be given in two extensive modules, each consisting of one week.


Assessment

Portfolio assessment of laboratory reports and home assignments counting about 40 % and oral examination counting about 60 %. All modules in the portfolio are assessed as a whole and one combined grade is given.

Assessment scale: A-F

Re-sit examination (section 22): There is no access to a re-sit examination in this course.

Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered postponed examination. Postponed assignments are arranged during the semester if possible, otherwise early in the following semester. Postponed final examination is held early in the following semester.

See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.

 

Coursework requirements Access to the final oral examination requires submission of the portfolio assignments.