PhD Information
This wiki hopes to cover most information regarding the admin part about doing a PhD at IMK, however there are many other places that also supply information and some are listed here.
General Information for PhD Students
A nice overview about doing a doctorate at IMK(TRO) can be found here.
PhD Council and Representatives
We have now started a PhD Council at IMK with two representatives in charge of the contact with the group leaders. We are informally in charge of adding things to this wiki as well as other projects for improving PhD life at IMK. If you are interested in joining us or you have small or big ideas send us an email at imk-phd-council@lists.kit.edu!
We are aiming at organising a bi-annual PhD Assembly to improve our networking as well as an opportunity to voice any concerns. These will hopefully be in conjunction with the semester start and will be announced in the TRO Newsletter as well through the PhD emailing lists.
Business trips
Please see the page on business trips.
On-boarding Checklist
Registering as a PhD Student
The registration procedure can be quite complicated for a new PhD student, here is the information given on the latest PhD Induction day.
Further information can be found here:
- The official document about the "Promotionsordnung" (only in German)
Teaching
A nice intro to teaching at the IMK can be found here.
Essential Software
The following is a list of essential software packages and the links to their web pages:
CDO
https://code.mpimet.mpg.de/projects/cdo/.
Note that CDO has a lot of built-in functions that are not well documented, but details about these can be usually found in their discussion forums, https://code.mpimet.mpg.de/projects/cdo/boards.
Python
Instructions on how to download and install Python for all OSs can be found at: https://www.python.org/.
It is usually recommended to use the Anaconda distribution to install Python. Details on how to do this are here: https://www.anaconda.com/
Python can be combined with a good Integrated Development Environment (IDE) of your choice. All existing IDEs have their pros and cons. Some of the most popular IDEs are the following:
Python boasts a large number of packages. Some of the most used packages for manipulating large files in NetCDF, HDF5 or CSV formats are the following:
Data analysis:
Parallel computing, Machine learning, Deep learning etc.:
Data visualization:
Most of these packages are distributed through either Conda [23] or Pip [24].
On top of these, various users also create packages tuned for specific purposes. They are usually made public through GitHub [25].
Various forums exist for the sole purpose of clearing specific questions about coding. One such forum is stackoverflow[26]. Medium [27] is also a good source for reading up about new ideas and tools in Python and other languages.