University of Salzburg
Salzburg | Website
Short Description
The Scientific Cluster Salzburg 1 consists of 3 identical server systems with Intel-Xeon(R) Gold 6144 processors @ 3.50GHz (max. 4.20GHz) v4, 4-sockets (32 cores) and 1.5 TB RAM, for scalable applications that have to address all cores in a single system. The base operating system is Red-Hat Linux. The system is connected to the university's computer center via a 10Gbit network.
Contact Person
Prof. DI Dr. Andreas Schröder
Research Services
Provision of high performance computing
Methods & Expertise for Research Infrastructure
Mathematics
Complex simulations and numerical applications, as well as calculations of linear models (multivariate statistics) and sample size planning. In particular, simulations of natural scientific and technical processes with finite element methods (andreas.schroeder@sbg.ac.at).
Informatics
Use of methods from algorithmic theory and of randomized algorithms (especially for the analysis of large networks). These techniques are combined with methods of discrete mathematics (robert.elsaesser@sbg.ac.at).
Biology
Computations in the field of community ecology: e.g. simulation of the influence of assembly-rules and intra-/interspecific competition to floral and vegetal traits of plant communities. Computations of relations of flower colours and bloom visitors with non-linear least square regressions.
Psychology
Used methods: FreeSurfer with Singularity, Dynamic Causal Modelling (DCM), ERF and time-frequency analysis
Expertise in: Longitudinal Structural MRI analysis, EEG / MEG analysis, Digital signal processing"
2015 - 2018
Univ.-Prof.Dr. Andreas Schröder
DFG
https://www.uni-salzburg.at/index.php?id=208518
High-order immersed-boundary methods in solid mechanics for structures generated by additive processes
2014 - 2022
Univ.-Prof.Dr. Andreas Schröder
DFG
https://www.uni-salzburg.at/index.php?id=208519
Funktionelle Reaktionen von Pflanzengemeinschaften und Blütenbestäuber-Interaktionen auf einen Höhengradienten und den Klimawandel (FWF P29142-B29)
2016-2019
Martin H. Lechleitner, Robert R. Junker
FWF
https://www.uni-salzburg.at/index.php?id=41115
Longitudinal structural changes in preclinical Alzheimer's disease
2019
Sara Fernandez-Cabello, Martin Kronbichler, Nathan Spreng and Taylor Schmitz
We use public data from ADNI: http://adni.loni.usc.edu/
Prior expectations and sensations shape perceptual decisions via distinct brain rhythms (under revision)
ongoing
Wislowska, M., & Schabus, M. fSON MEG (running)
Bauer, M., Wislowska, M., Veale, T., Morris, P.G., Liddle, P.F., Heekeren, H.R. & Brookes, M. (under revision)
The influence of memory strength for sleep-associated memory consolidation
2018
Heib, D.P.J., Bäuml, K.-H.; Klimesch, W., & Schabus, M.
Das virtuelle Schlaflabor: Digitale Schlafanalyse & Schlaf-Coaching X
2020-2022
Schabus, Manuel; Bathke, Arne; Borgelt, Christian & Heib, Dominik Philip Johannes & Stöggl, Thomas
Land Salzburg
https://uni-salzburg.elsevierpure.com/de/projects/das-virtuelle-schlaflabor-digitale-schlafanalyse-schlaf-coaching-
Generating predictions during sleep
2019-2022
Armeen, M.
ÖAW
https://uni-salzburg.elsevierpure.com/de/projects/generating-predictions-during-sleep
2018
Andreas Byfut, Andreas Schröder
Int J Numer Meth Engng.
https://onlinelibrary.wiley.com/doi/abs/10.1002/nme.5609
Marching volume polytopes algorithm
2019
Andreas Byfut, Friederike Hellwig, Andreas Schröder
Int J Numer Meth Engng.
https://onlinelibrary.wiley.com/doi/10.1002/nme.5995
Small-sample performance and underlying assumptions of a bootstrap-based inference method for a general analysis of covariance model with possibly heteroskedastic and nonnormal Errors
2019
Zimmermann G, Pauly M, and Bathke AC
Stat Methods Med Res, accepted, doi: 10.1177/0962280218817796
Sample size calculation and blinded recalculation for analysis of covariance models with multiple random covariates
2018
Zimmermann G, Kieser M, and Bathke AC
Journal of Biopharmaceutical Statistics, arXiv:1806.03673v1 [stat.ME]
The brain selectively tunes to unfamiliar voices during sleep
2022
Ameen, M., Heib, D. P. J., Blume, C., & Schabus, M.
Journal of Neuroscience
https://www.jneurosci.org/content/42/9/1791
Slow oscillation-spindle coupling strength predicts real-life gross-motor learning in adolescents and adults
2022
Hahn, M. A., Bothe, K., Heib, D., Schabus, M., Helfrich, R. F., & Hoedlmoser, K.
eLife
https://elifesciences.org/articles/66761
Slow oscillation-spindle coupling predicts enhanced memory formation from childhood to adolescence
2020
Hahn, M. A., Heib, D., Schabus, M., Hoedlmoser, K., & Helfrich, R. F.
eLife
https://elifesciences.org/articles/53730