... | @@ -10,4 +10,9 @@ Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BD |
... | @@ -10,4 +10,9 @@ Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BD |
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|Hermanns, Marc-André m.a.hermanns@fz-juelich.de | JSC Cross-Sectional-Team Parallel Performance | none yet (testing with Keras atm.) | Performance Data (profile and trace) | Identify or estimate performance phenomena | none |
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|Hermanns, Marc-André m.a.hermanns@fz-juelich.de | JSC Cross-Sectional-Team Parallel Performance | none yet (testing with Keras atm.) | Performance Data (profile and trace) | Identify or estimate performance phenomena | none |
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| Wagner, Christian c.wagner@fz-juelich.de | PGI-3 Molecular manipulation lab | general overview over ML principles, PCA, background on methods to combine ML and comptational chemistry, reinforcement learning with NNs | (hysteretic) scalars along (x,y,z), trajectories, computational, chemistry data, scalar time series | easy access to ML expertise, possibility to discuss (and potentially solve) individual ML problems / tasks at detailed level, potentially in the frame of a collaboration | none |
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| Wagner, Christian c.wagner@fz-juelich.de | PGI-3 Molecular manipulation lab | general overview over ML principles, PCA, background on methods to combine ML and comptational chemistry, reinforcement learning with NNs | (hysteretic) scalars along (x,y,z), trajectories, computational, chemistry data, scalar time series | easy access to ML expertise, possibility to discuss (and potentially solve) individual ML problems / tasks at detailed level, potentially in the frame of a collaboration | none |
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| Hagemeier, Björn b.hagemeier@fz-juelich.de | JSC Project Helmholtz Analytics Framework | Basic ML methods | | | |
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| Hagemeier, Björn b.hagemeier@fz-juelich.de | JSC Project Helmholtz Analytics Framework | Basic ML methods | | | |
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| Stadtler, Scarlet s.stadtler@fz-juelich.de | JSC Federated Systems and Data Division | Absolute Beginner (At JSC courses in ML and DL) | Meteorological four dimensional Data (space and time) | meet ML experts, possibility to discuss individual DL problems | |
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| Goergen, Klaus k.goergen@fz-juelich.de | IBG-3 Integrated Modelling | various supervised and unsupervised methods,
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PCA, CCA, SOMs; not used recently though | Regional climate model outputs, meteorological observations | big data (200-300TB) capable data analytics frameworks | none |
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| Krajsek, Kai k.krajsek@fz-juelich.de | JSC HPC in Neuroscience | Supervised Learning Unsupervised Learning Meta-Learning, Inverse Modelling with ML, Deep Learning in Computer Vision, Probabilistic Inference, Gaussian Processes | Meteorological model output (4D space-time volumes), Diffusion MRI data | |
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