Profiles

Inga Koszalka

Lektor

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Works at Department of Meteorology
Telephone 08-16 43 14
Email inga.koszalka@misu.su.se
Visiting address Svante Arrhenius väg 16 C
Postal address Meteorologiska institutionen (MISU) 106 91 Stockholm

About me

For more information, please visit my external home page: https://sites.google.com/site/ingamonikakoszalka/

Jobs:

I have 2 PhD positions still available! For details, please see here and contact me if you are interested!

The Department of of Meteorology (MISU) has an opening for a Postdoctoral Fellow in Machine Learning for Applications in Climate Science (https://www.su.se/english/about/working-at-su/jobs?rmpage=job&rmjob=9685&rmlang=UK). The potential applicants are encouraged to contact the scientists at MISU involved in the Swedish e-Science Centre. If you are interested in applying Machine Learning to ocean observations, please see here for details and contact me.
 

Previous teaching experience: (7 semesters, 17 course contributions including cruises and a self-designed course on Lagrangian analysis and turbulent dispersion in the ocean), workshops and more. For more details, see: teaching

Teaching at the Department of Meteorology, Stockholm University:

I am teaching a Master Course Geophysical Fluid Dynamics in September 2019, Fall term A (link). The course is focused on a one of the most fundamental model for the fluid motion in atmosphere and ocean, the shallow-water equations, which helps understand various dynamic phenomena. The course considers geostrophic adjustment, the separation onto slow and fast mode, reduction to quasi-geostrophy, waves solutions (e.g. Rossby waves and gravity waves), conservation laws and stability theory. Apart of theoretical derivations and examples for observations of the dynamical phenomena addressed with the theory, the course includes exercises running computer simulations and experiments with a rotating table. 

For more information about the GFD course and how to apply, please visit the course webpage: link. The second registration period for the course opens on 15.07.2019! 

 

Left: A snapshot from my own simulation of quasi-geostropic turbulence approximating mesoscale eddy field in the ocean, marked by a biological tracer. See also Koszalka et. al., 2007 (https://doi.org/10.1016/j.tpb.2007.03.007). Middle: Mean relative dispersion of surface drifter pairs in the eastern Nordic Seas (most of them deployed by myself during oceanographic cruises). One such pair is shown on the right panel. From the scaling laws revealed by the relative dispersion in function of time, kinetic energy spectra of ocean currents in this region can be inferred, which we found akin to those in quasi-geostrophic flows. See also Koszalka et. al., 2009 (https://doi.org/10.1357/002224009790741102).

 
I am also teaching a Master Course Physical Oceanography in November-January 2019, Fall term C (link). The updated course schedule and content will be published latest in September. The course considers central concepts and dynamical phenomena in physical oceanography. A new feature of the course this year will be an oceanographic cruise with R/V ELECTRA that I am organizing right now.
 
 
For more information about the PO course and how to apply, please visit the course webpage: link. The second registration period for the course opens on 15.07.2019! 

 

I am organizing two Workshops on “Deep Neural Networks for Beginners” taught by Prof. Ribana Roscher (University of Osnabrück, Germany). These two workshops are inspired by a successful workshop we organized in Kiel, Germany, in February, 2019. The workshops address applications of neural networks in different fields of science and target PhD students, researchers and faculty from  Natural Sciences, Medicine, but also Applied Mathematics and Computer Science. The workshops consist of lectures and hand-on tutorials with example applications to time series prediction and image feature recognition using Python Keras/Tensor flow. You will learn about basic methods for building a neural networks architecture and what are simularities and differences between applications like prediction of fungal diseases on crop plant species and mesoscale eddy detection in the ocean.