IDP M³OCCA – Measuring and Modelling MOuntain glaciers and ice caps in a Changing ClimAte

International Doctoral Program granted by the Bavarian State Ministry of Science & Art

The International Doctorate Program (IDP) “Measuring and Modelling Mountain glaciers and ice caps in a Changing ClimAte (M³OCCA)” will substantially contribute to improving our observation and measurement capabilities of mountain glaciers and ice caps by creating a unique inter- and trans-disciplinary research and training platform. IDP is a collaboration of scientists from geography, geology, geophysics, computer sciences, electrical engineering and mathematics. Thematically, we will address main uncertainties of current measurements in glaciology and permafrost by developing new instruments and future analysis techniques as well as by considerably advancing geophysical models. IDP will have a strong component of evolving techniques in the field of deep learning and artificial intelligence (AI) as the data flow from Earth Observation (EO) into modelling increases exponentially. Within the IDP we combine cutting edge technologies with climate research. We will develop future technologies and transfer knowledge from other disciplines into climate and glacier research.

IDP M³OCCA is a collaboration between FAU Erlangen-Nürnberg, the Technical University of Munich, the Microwave Institute of the German Aerospace Center(DLR) in Oberpfaffenhofen and the Bavarian Academy of Sciences (BAdW) located in Munich. for its research, IDP M³OCCA will leverage on Bavaria’s existing long-term commitment via the super test site Vernagtferner in the Ötztal Alps run by Bavarian Academy of Sciences (BAdW). In addition, we cooperate with the University of Innsbruck and its long-term observatory at Hintereisferner both located in the Ötztal Alps in Austria. At those super test sites, we will perform joint measurements, equipment tests, flight campaigns and cross-disciplinary trainings and exercises for our doctoral researchers. We will leverage on existing instrumentation, measurements and time series. As a consequence, several of the sub-projects require field activities on glacier sites.

IDP M³OCCA aims at educating emerging talents with an interdisciplinary vision as well as excellent technical and soft skills. It comprises nine doctoral positions funded directly from our program and eleven affiliated doctoral candidates. Our doctoral researchers will be guided by interdisciplinary, international teams comprising university professors, senior scientists and emerging talents from the participating universities and external research organizations.

You can find a Podcast to IDP M³OCCA on FAU Campusradio Funklust”.

We look for high potential international doctoral candidates in each of the 9 sub-projects. The call is open until positions are filled, however, screening will start March 01st, 2022. Please clearly indicate in the subject of your application for which of the sub-projects you apply. In case you are interested in several of the SPs, please provide a priority. Applications shall be directed to The IDP and all positions will start June 01st, 2022 and will have a duration of 4 years. For specific questions on the sub-projects please contact the respective supervisor, for general questions on the IDP the coordinator (M. Braun, FAU Geography). position announcement as PDF.

The Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is seeking to augment the number of women in research and teaching and specifically addresses female scientists to apply for this position. We further encourage persons with disabilities to apply. If desired, a member of the equal opportunity office of FAU can participate in the selection process without any disadvantage for the applicant.

Sort descriptions of sub-projects:

SP1.1: A lightweight multifrequency radar system for snow and firn structure measurements (FAU)

Typically ground based GPR systems for geophysical applications consist of low frequency pulse radars which operate in the MHz frequency range. As those rudimentary radar concepts have proven to be easy to use and offer enough resilience to endure in rough environments in the past, the most recent and enormous advances in radar technology have almost exclusively been made in other fields such as automotive radar or personnel security scanner systems. Innovative GPR approaches can strongly benefit from these new radar techniques e.g. by multi-functional radar systems that utilize software defined waveforms and measuring principles best adapted to the specific environment and measuring task. A holistic hardware-software design based on cutting-edge technology is needed to obtain the maximum performance for the intended field of use. By this, enhanced mapping and surveying of sub-glacial structures will be achieved, and previously unsolved challenges linked to the measurement of ice and snow parameters in remote locations will be effectively addressed. A lightweight and easy to transport radar system will be developed within this project which will serve as a versatile sensor setup for multiple applications in remote glacial environments. For this, a multi frequency system will be implemented which will make use of the benefits of different frequency bands. E.g., typical GPR systems for measuring ice sheet thicknesses on alpine glaciers operate in the sub 200-MHz region as signals experience less attenuation but can only obtain comparably small bandwidths and therefore low resolution. Imaging Radars working in the >1GHz frequency range suffer from higher attenuation but can reach outstanding resolution. Those and further advantages will be combined for maximal performance and flexibility adapted to each scenario. For this purpose, suitable high performance and meanwhile power and weight saving transceiver frontend concepts need to be designed and tested for their usability. Furthermore, innovative antenna concepts need to be examined in order to address the requirements of lightweight and small size while maintaining excellent radiation characteristics over a wide / multi band frequency range. With the integration of localization techniques such as differential GNSS and other advanced self-localization techniques it will be possible to apply ground based SAR techniques for enhanced resolution and sensitivity of the overall system. The integrated digital backend will exploit cutting edge radar algorithms which have to be expanded in order to combine data taken in different frequency bands for extracting an even wider range of parameters from the surveyed ice structures compared to a single band evaluation. Examples for those results are amongst other things estimated water content in the snow/firn cover or the glaciers permittivity profile which is directly linked to the ice density. Integrated into vehicles such as UAVs the system will be capable of surface based mapping of large areas in a time and cost-effective manner. The system will be tested extensively on Vernagt- and Hintereisferner. The sub-project will collaborate with SP1.2, 1.3, 2.3 and 3.1.


For specific information on the sub-project please contact: Prof. Dr.-Ing. Martin Vossiek; LHFT, Institute of Microwaves and Photonics (LHFT), Cauerstr. 9, 91058 Erlangen, T: 09131-85-20773,,

Co-PIs: G. Krieger (DLR HR), T. Seehaus (FAU Geography), F. Navarro (U. Madrid)


SP1.2: SAR tomography for 3D imaging of snow and firn structures (FAU/DLR)

Structures in the firn and snow cover on glaciers can be caused by annual melt/freeze cycles, but also by internal water percolation or refreezing. Additionally, water channels within the glaciers are important indicators for melt water routing in geophysical and hydrological glacier models. The highest changes occur within the upper layer (first tens of meters) and are therefore of importance to be monitored. Synthetic Aperture Radar (SAR) tomography is an evolving 3D imaging technique that enables the mapping of subsurface properties of glaciers and ice sheets with high spatial resolution, taking advantage of the penetration of radar signals up to several tens of meters into dry snow, firn, and ice (Tebaldini et al., 2016, Fischer et al., 2019). The main objective of this doctoral project is to exploit and improve this powerful 3D imaging technique and to establish the relation of the vertical reflectivities to geophysical snow/ice parameters. Moreover, new tomographic imaging modes and techniques like transmission, MIMO and subaperture-based tomography will be explored in view of their potential to gain further information about the internal structure and dielectric properties of snow and glaciers. In the frame of the project dedicated campaigns on Vernagtferner and Hintereisferner will be conducted, where multiple sensors collect data to estimate and characterise the internal structures of snow and ice regions. For this, the airborne multi-modal SAR system of DLR, as well as ground-based laser and radar systems will be deployed to acquire both multi-angular and multi-temporal data. Simultaneously, point/grid-based measurements and satellite data will be collected and evaluated. Strong links exist to SP1.1, SP1.3, SP2.2, SP2.3 and SP3.1.

For specific information on the sub-project please contact: Prof. Dr.-Ing. Gehard Krieger, LHFT, Institute of Microwaves and Photonics (LHFT), Cauerstr. 9, 91058 Erlangen, T: 08153-28-3054,

Co-PIs: I. Hajnsek (DLR HR / ETH ZH), C. Mayer (BAdW), H. Rott (ENVEO IT)


SP1.3: Machine learning on radargrams (FAU)

Information on ice thickness and internal structures of ice bodies (e.g. water table, isochrones, water pockets, and channels) from ground penetrating radargrams is to date often picked manually. Often only a specific target neglecting all other information is traced since existing contour-following algorithms in standard software like REFLEX do not provide consistent and reliable output. Within this doctoral project, we aim at using and modifying machine learning techniques from medical imaging as well as natural language processing (NLP) and apply those to glaciological radargrams. Ice thickness, bedrock topography, as well as internal structures, shall be mapped ideally at once after the respective pre-processing of the radargrams has occurred. Each radargram is composed of lines denoting different structures in the ice body. Algorithmically, this represents a segmentation problem in radargrams.
Datasets are available from airborne and ground-based low-frequency radar surveys of sites in the Alps and high Mountain Asia (BAdW), planned campaigns in Patagonia and alpine-type glaciers in Antarctica (FAU). Additional material for algorithm testing and training is available through an intense collaboration with AWI from polar surveys. Ideally, the developed algorithms will also be tested on the first survey data of the developed new multi-frequent radar in sub-project 1.1. Further links exist also to SP 2.3, and 3.1.

Applicants should have a strong background in Deep Learning and have preferably worked on segmentation tasks before as well as having prior knowledge about transformer architectures.

For specific information on the sub-project please contact: Dr. Vincent Christlein; Department of Computer Sciences, Pattern Recognition Lab, Martensstraße 3, 91058 Erlangen, T: 09131-85-20281,,

Co-PIs: A. Maier (FAU Informatics), T. Seehaus (FAU Geography), F. Navarro (U. Madrid)


SP2.1: Glacier outlines from optical and SAR imagery by deep learning (FAU)

Glacier extent is an ECV and various international attempts exist to harmonize a global glacier inventory data set with outlines if possible from multiple data sources (GLIMS, IASC Randolph Glacier Inventory, RGI). However, to date no real operational large-scale repeat mapping capabilities exist. Glacier outlines are required at different time intervals e.g. with matching observational intervals for specific mass balance computations out of remote sensing measurements (e.g. for comparison with in-situ observations or on regional scale) or to validate ice dynamic modelling. In particular for small fast changing glaciers area updates are important since errors from area mismatch are highest. While for calving glaciers various deep learning approaches exist outlines of land-terminating glaciers are often more difficult to delineate, in particular when the glacier tongue is debris-covered. We aim at an approach that integrates the advantages of synthetic aperture radar and optical observation capabilities and analysis techniques. The candidate is supposed to advance our existing processing setup and to develop and systematically test a new method. Within this doctoral project we aim at training a machine learning algorithm using multi-temporal SAR coherence images jointly with other data. This SP has strong thematic links to SP3.2 and 3.3 and methodological to SP 2.2.

Applicants should have a strong background in Deep Learning and have preferably worked on segmentation tasks before. Knowledge on processing of SAR remote sensing and optical data is of advantage.

For specific information on the sub-project please contact: Prof. Dr. Matthias Braun, Institut für Geographie, FAU, Wetterkreuz 15, 91058 Erlangen, T: +49 9131-85-22015,,

Co-PIs: E. Bänsch (FAU Mathematics), P. Rizzoli (DLR HR), M. Zemp (Univ. ZH)


SP2.2: Radar penetration of TanDEM-X on glaciers & ice capsSAR tomography for 3D imaging of snow and firn structures (DLR)

The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020). The uncertainty is highest in presence of dry snow, not affected by melting phenomena as in the accumulation area of high mountain glaciers or polar glaciers and ice caps outside the large ice sheets. Within this sub-project, we aim at estimating radar penetration by deploying a deep learning architecture using input data from SAR imagery like coherence, backscatter, frequency and geometric baseline as well as a terrain model. As reference data for training deep learning networks in a supervised manner, we will utilize the height difference between precise laser altimetric measurements (ICESat-2, Operation IceBridge, ka-band data from JPL UAVSAR system) for the surface and radar DEMs for the location of the mean interferometric phase center. Methodologically, we suggest the use of a deep convolutional network to estimate the altimetry, phrasing it as a regression problem. In particular, we propose the adoption of AdaIn-based Tunable CycleGAN (Gu 2020). We believe the additional CycleGAN constraints in combination with the switchable adaptive instance normalization will serve as a valuable regularizer. Making the CycleGAN tunable and hence, omitting one generator from the original CycleGAN approach, leads to a significant reduction in memory requirements and a more stable training process even on small datasets. Additionally, we can incorporate other data such as the local incident angle as well as climatological information as conditional input for the discriminators, since both variables can significantly influence the interferometric SAR phase center depth below the actual surface. This SP is linked to SP 1.2 with a joint airborne survey as well as to SP 2.3 and SP 3.1.

For specific information on the sub-project please contact: Dr.-Ing. Paola Rizzoli, Münchener Straße 20, 82234 Weßling, T: 08153-28-1785,,

Co-PIs: M. Braun (FAU Geography), A. Maier (FAU Informatics), P. Millilo (Univ. Houston)


SP2.3: Improved volume to mass conversion (BAdW)

Glacier mass balance variations are an essential indicator for regional and global climate fluctuations. Today, glacier volume changes can be detected with satellite based systems to a reasonable degree of accuracy, while density estimates of the affected volume are still based on models and a priori assumptions. Even though, methods for volume to mass conversion are established, there exists a considerable knowledge gap with respect to validation and temporal stability of the assumptions. Especially the transient evolution of the firn pack will strongly influence the density assumptions of the glaciers with time. During periods of negative mass balances, the shrinkage of the snow and firn resources requires an adaptation of the usual conversion techniques. In addition, there is a strong need to investigate the regional variability of density distribution, as the glacier-climate relation is strongly dependent on the regional characteristics like topography, precipitation and wind patterns.

A large data basis exists for Vernagtferner in the Ötztal, related to the temporal development of the glacier and its firn regions. Similar information is available for the adjacent Hintereisferner, making this region an ideal test site for such investigations. A detailed investigation of firn and snow deposits, based on glaciological and geophysical methods and microwave remote sensing instruments should be conducted to provide a clear knowledge about the firn pack architecture and its spatial distribution. In combination with legacy remote sensing observations of firn and snow extents and volume changes, regional climate information will be used to investigate the temporal development of glaciers and their compartments. This analysis should lead to a strongly improved approach for volume to mass conversion, especially with regard to temporal and regional variability, which is also essential for large-scale mass balance assessments. Direct employment of the results in, and exchange with the modelling-orientated sub-projects will be possible and highly welcome.

We look for a candidate, who is interested in the role of the snow and firn cover on glaciers with respect to their impact on regional glacier mass balance estimates.

The candidate should have a solid basis in glaciology and geophysical applications, especially with respect to ground penetrating radar techniques. In addition, knowledge on snow physics is an advantage. Experience in glacier travel and field work in a harsh environment is required, due to the need of acquiring in-situ data at Vernagtferner and other glaciers. As this post is located at FAU Erlangen, but supervised at BAdW in Munich, the candidate will have the opportunity to gain expertise from both research groups.


For specific information on the sub-project please contact: Dr. Christoph Mayer, Alfons-Goppel-Str. 11 (Residenz), 80533 München, T: 089-23031-1260,,

Co-PIs: T. Mölg (FAU Geography), M Huss (ETH ZH), R. Hock (Univ. Oslo)


SP3.1: Targetting snow drift and refreezing in glacier mass budgets with machine learning (FAU)

Glacier surface mass balance (SMB) modelling has traditionally focused on improving the determination of surface energy balance conditions. However, water routing in the firn including refreezing, as well as the complex patterns of surface accumulation caused by snow drift, are poorly, or not at all, represented in most SMB models. Studies in the literature have often noted the uncertainties due to neglecting such mechanisms. This sub-project aims to improve the model representation of (i) the internal water budget and (ii) the snow drift contribution to the SMB using deep learning. This aim links ideally with ongoing snowdrift measurements on Hintereisferner in the frame of a running joint project between FAU and the University of Innsbruck ( It furthermore links very well with our strong background in SMB modelling and developing process-based model parameterizations. We hypothesize, however, that the accuracy of such parameterizations can be increased drastically by including machine learning algorithms and new measurements on snow drift dynamics. In this subproject, we will explore the associated added value by sensitivity simulations and analyses of the resultant mass and energy balance components. An improved knowledge of the mechanisms in question is also important for the precise estimation of sea level contributions from glaciers measured from altimetry and DEM differencing (SP2.2 and 2.3), since the measured elevation changes have to be converted to mass using a certain density assumption. The SP also links to SP1.2 and SP1.3 where internal structures of the snow and firn cover are mapped.


Applicants: You should have a background in numerical modeling and, ideally, in climatology/meteorology of mountains and cold regions with a link to the cryosphere. A keen interest in machine learning is a pre-condition as well, while field experience is a bonus.


For specific information on the sub-project please contact: Prof. Dr. Thomas Mölg, Institut für Geographie, Wetterkreuz 15, 91058 Erlangen, T: 09131-85-22633,,

Co-PIs: L. Nicholson, R. Prinz (Univ. Innsbruck), R. Hock (Univ. Oslo)


SP3.2: Reconciling machine learning and glacier system modelling (FAU)

Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.


Detailed Information and position requirements of this SP are formulated in this PDF.

For specific information on the sub-project please contact:
Dr. Johannes Fürst (NGL)
Department of Geography and Geosciences, Institute of Geography
Wetterkreuz 15, 91058 Erlangen, T: 09131-85-26680,
Prof. Dr. Harald Köstler
Department of Computer Sciences, System Simulation
Cauerstraße 11, 91058 Erlangen, T: 09131-85-29359,

I. Tabone (FAU Geography)
O. Galiardini (Univ. Grenoble)
F. Maussion (Univ. Innsbruck)


SP3.3: Mass movement assessment and modelling in recent ice-free areas (TUM)

The retreat of glacier ice fundamentally changes stability of the surrounding rock and debris slopes due to the massive thermal, mechanical and hydrological change initiating massive creep and rock slope failures (Haeberli et al., 2010). Alpine and arctic polythermal glaciers develop permafrost below the upper cold-based glaciers and unfrozen rock below the warm based lower glacier parts. Glacier retreat and warming cause high rates of permafrost degradation and sometimes aggradation, sudden changes in hydro- and cryostatic pressures and fast changes in lateral ice-stresses on slopes (Krautblatter & Leith, 2015). In SP3.3, we will develop generic mechanical models that undergo rapid thermal, mechanical and hydrological change. Release of ice stress and thermal conditions will be refined by additional information on glacier retreat (SP2.1) and on simulated basal ice temperatures (SP3.2). For the latter temperatures, SP3.3 will build on the finite-element Elmer/Ice applications calibrated to the benchmark glaciers for the last decades (SP3.2). Here, these simulations will be extended to cover the last century. The presumable starting date is the end of the Little Ice Age (LIA). The modelling strategy is an initial equilibration run under LIA conditions followed by a forward simulation to present day forced by climatic data (e.g. ERA-20C, ERA5, CORDEX-CORE) downscaled to local AWS measurements (joint effort with SP3.1). The centennial scope assures that the long-term memory is imprinted in the 3D temperature field. Accounting for both retreat and temperature information, we will develop generic rock-ice-mechanical models using the benchmark sites Vernagtferner and Hintereisferner. The benchmark model will apply temperature-dependent changes in rock stability, ice stability in fractures and rock-ice interfaces as well as changes in hydro- and cryostatic forcing and lateral destabilisation due to reducing glacier support (Krautblatter et al. 2013). Model development will occur on basis of discontinuum mechanical (UDEC) models that have been developed in recent research projects (Mamot et al., 2020). The generic mechanical models will be clustered to specific conditions indicated by glacier retreat and elevation change information outside glaciers to constrain models that anticipate the propensity of major hazards due to rock slope and debris slope failures in the recently ice-free slope and glacier forefields.


For specific information on the sub-project please contact: Prof. Dr. Michael Krautblatter, Ingenieurfakultät Bau, Geo & Umwelt, Arcisstr. 21, 80333 Munich, T: 089-289-25866,,

Co-PIs: J. Fürst (FAU Geography), B. Etzelmüller, S. Westermann (Univ. Oslo)


The education program of IDP M³OCCA comprises regular compulsory lectures, an annual joint retreat as well as workshops. Depending on the background of the doctoral candidate the supervising team will define an additional set classes to be taken in the first year.