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| Name (Last, First), mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs |
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| Name (Last, First), mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs |
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|:---|:---|:---|:---|:---|
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| Jitsev, Jenia j.jitsev@fz-juelich.de | JSC Deep Learning Research Lab (CST-DL) | Unsupervised Learning, Reinforcement Learning, Recurrent Hierarchical Winner-Take-All Networks, Generative Models, Biological Neural Networks, Open-end learning / Learning to learn | Images, Time Series, Virtual Environments (e.g, OpenGym) | Large scale scientific data sets (material science, high throughput genomics/proteomics, biotechnology, etc) |
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| Axer, Markus m.axer@fz-juelich.de | INM-1 Fiber Architecture Group | none | Microscopic resolution 2D and 3D images, scalar and vector valued data |
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| Wenzel, Susanne s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | |
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| Comito, Claudia c.comito@fz-juelich.de | JSC - Helmholtz Analytics Framework (HAF) | Currently human-learning | | Probabilistic approach |
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Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Image Segmentation, Image Feature detection, Markov Random Fields, Clustering, Analogies to the human brain| Microscopic resolution 2D and 3D images | | |
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| Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Image Segmentation, Image Feature detection, Markov Random Fields, Clustering, Analogies to the human brain| Microscopic resolution 2D and 3D images | | |
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| Kraus, Jiri jkraus@nvidia.com | NVIDIA | | | Want to learn the ML/DL needs of scientist at FZJ |
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| Gohlke, Holger h.gohlke@fz-juelich.de | JSC / ICS-6 / NIC Research Group | Deep learning / Supervised/unsupervised learning | Molecular structure data | meet ML experts, possibility to discuss individual DL problem |
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|Zimmermann, Olav olav.zimmermann@fz-juelich.de | JSC Simlab Biology | Supervised learning (SVM and its variants), Dimension Reduction (Isomap et al, clustering), Metaheuristics, basics of sequence learning methods (CRF, LSTM), bioinformatics | biological sequence data, molecular structure data, experimental data (2-dim to n-dim or graphs), unstructured data | ML for hypertoroidal output spaces in depth understanding of seq2seq methods, representation of world knowledge for learning methods that work with non-differentiable loss |
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|Herten, Andreas a.herten@fz-juelich.de | JSC NVIDIA Application Lab | Software installation | | Overview/support of ML/DL software in Jülich |
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| Göbbert, Jens Henrik j.goebbert@fz-juelich.de | JSC Cross-Sectional-Team Visualization | Basic DL with Keras | turbulent flows (3D) | Collaborations/support for enabling DL with on HPC with Jupyter |
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| Göbbert, Jens Henrik j.goebbert@fz-juelich.de | JSC Cross-Sectional-Team Visualization | Basic DL with Keras | turbulent flows (3D) | Collaborations/support for enabling DL with on HPC with Jupyter |
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| Grießbach, Sabine s.griessbach@fz-juelich.de | JSC SimLab Climate Science | none | satellite data, infrared spectra, meteorological data | unsupervied ML |
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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 |
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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 |
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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 |
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| Herten, Andreas a.herten@fz-juelich.de | JSC NVIDIA Application Lab | Software installation | | Overview/support of ML/DL software in Jülich |
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| Jitsev, Jenia j.jitsev@fz-juelich.de | JSC Deep Learning Research Lab (CST-DL) | Unsupervised Learning, Reinforcement Learning, Recurrent Hierarchical Winner-Take-All Networks, Generative Models, Biological Neural Networks, Open-end learning / Learning to learn | Images, Time Series, Virtual Environments (e.g, OpenGym) | Large scale scientific data sets (material science, high throughput genomics/proteomics, biotechnology, etc) |
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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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| Kraus, Jiri jkraus@nvidia.com | NVIDIA | | | Want to learn the ML/DL needs of scientist at FZJ |
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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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| 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, PCA, CCA, SOMs; not used recently though | Regional climate model outputs, meteorological observations | big data (200-300TB) capable data analytics frameworks |
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| Goergen, Klaus k.goergen@fz-juelich.de | IBG-3 Integrated Modelling | various supervised and unsupervised methods, PCA, CCA, SOMs; not used recently though | Regional climate model outputs, meteorological observations | big data (200-300TB) capable data analytics frameworks |
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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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| Pleiter, Dirk d.pleiter@fz-juelich.de | JSC Technology Department | Architectures optimized for DL, requirements analysis | | |
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| Pleiter, Dirk d.pleiter@fz-juelich.de | JSC Technology Department | Architectures optimized for DL, requirements analysis | | |
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| Arasan, Durai d.arasan@fz-juelich.de | INM-1 Connectivity | Support Vector Machines, Random Forests, Neural Networks | 3D MRI data | Developing ML algorithms for neuroimaging data, Learning Deep Learning, feature engineering and optimization |
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| Arasan, Durai d.arasan@fz-juelich.de | INM-1 Connectivity | Support Vector Machines, Random Forests, Neural Networks | 3D MRI data | Developing ML algorithms for neuroimaging data, Learning Deep Learning, feature engineering and optimization |
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| Huysegoms, Marcel m.huysegoms@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Markov Random Fields, Clustering | Microscopic-resolution 2D and 3D cyto images | |
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| Huysegoms, Marcel m.huysegoms@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Markov Random Fields, Clustering | Microscopic-resolution 2D and 3D cyto images | |
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... | @@ -21,14 +22,13 @@ Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BD |
... | @@ -21,14 +22,13 @@ Dickscheid, Timo t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group (BD |
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|Campos, Lucas l.campos@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | None; JSC courses on Machine Learning/Deep Learning. Weekend fun with SVM and Neural networks way back when | 3D MRI data | |
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|Campos, Lucas l.campos@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | None; JSC courses on Machine Learning/Deep Learning. Weekend fun with SVM and Neural networks way back when | 3D MRI data | |
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| Dammers, Jürgen j.dammers@fz-juelich.de | INM-4 MEG Group | A bit of supervised Deep Learning techniques (CNN and DNN) | 4D MEG data | Developing unsupervised deep learning strategies for the analysis of large sets of 4D neuroimaging data |
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| Dammers, Jürgen j.dammers@fz-juelich.de | INM-4 MEG Group | A bit of supervised Deep Learning techniques (CNN and DNN) | 4D MEG data | Developing unsupervised deep learning strategies for the analysis of large sets of 4D neuroimaging data |
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| Cavallaro, Gabriele g.cavallaro@fz-juelich.de | JSC High Productivity Data Processing Group | Supervised learning, Unsupervised learning, Feature engineering | Remote sensing data (optical and SAR) | |
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| Cavallaro, Gabriele g.cavallaro@fz-juelich.de | JSC High Productivity Data Processing Group | Supervised learning, Unsupervised learning, Feature engineering | Remote sensing data (optical and SAR) | |
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| Axer, Markus m.axer@fz-juelich.de | INM-1 Fiber Architecture Group | none | Microscopic resolution 2D and 3D images, scalar and vector valued data |
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| Khalid, Fahad f.khalid@fz-juelich.de | JSC SimLab Neuroscience | 1) Experience in using CNNs for image segmentation. 2) Overview of literature on distributed training and inference for deep networks. 3) Setting up and testing DL frameworks on HPC infrastructure. | 1) Very high resolution 2D images captured using the Polarized Light Imaging technology 2) Low resolution volumetric image data from Magnetic Resonance Tomography | Hardware infrastructure for training and inference, which is readily available at the JSC |
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| Khalid, Fahad f.khalid@fz-juelich.de | JSC SimLab Neuroscience | 1) Experience in using CNNs for image segmentation. 2) Overview of literature on distributed training and inference for deep networks. 3) Setting up and testing DL frameworks on HPC infrastructure. | 1) Very high resolution 2D images captured using the Polarized Light Imaging technology 2) Low resolution volumetric image data from Magnetic Resonance Tomography | Hardware infrastructure for training and inference, which is readily available at the JSC |
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| Glock, Philipp p.glock@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Image Segmentation, pytorch, SVM | Microscopic-resolution 2D and 3D cyto images | |
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| Glock, Philipp p.glock@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Deep Learning with ConvNets, Image Segmentation, pytorch, SVM | Microscopic-resolution 2D and 3D cyto images | |
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| Comito, Claudia c.comito@fz-juelich.de | JSC - Helmholtz Analytics Framework (HAF) | Currently human-learning | | Probabilistic approach |
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| Haas, Sarah sa.haas@fz-juelich.de | INM-1 Big Data Analytics Group (BDA | Deep Learning with ConvNets, Image Feature Detection | Microscopic-resolution 2D and 3D cyto images | |
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| Haas, Sarah sa.haas@fz-juelich.de | INM-1 Big Data Analytics Group (BDA | Deep Learning with ConvNets, Image Feature Detection | Microscopic-resolution 2D and 3D cyto images | |
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| Ungermann, Jörn j.ungermann@fz-juelich.de | IEK-7 | Inverse Modelling, Linear Regression | 2-D and 3-D interferograms | |
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| Nöh, Katharina k.noeh@fz-juelich.de | IBG-1: Biotechnology, Modeling and Simulation Group | Image segmentation with DL (U-Nets) | Large-scale microscopic images from time-lapse microscopy (2D, x-TB range) | Meet DL/ML experts, discuss DL solutions for specific applications |
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| Schober, Martin m.schober@fz-juelich.de | INM-1 Fiber Architecture Group | Beginner | Microscopic resolution 2D and 3D images, scalar and vector valued data | |
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| Schlottke-Lakemper, Michael m.schlottke-lakemper@fz-juelich.de | JSC/JARA SimLab Fluids & Solids | Beginner | Continuum mechanics (fluids, solids) | Meet like-minded researches & ML experts to exchange ideas, learn who to ask in case of specific problems/questions |
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| Schlottke-Lakemper, Michael m.schlottke-lakemper@fz-juelich.de | JSC/JARA SimLab Fluids & Solids | Beginner | Continuum mechanics (fluids, solids) | Meet like-minded researches & ML experts to exchange ideas, learn who to ask in case of specific problems/questions |
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| Grießbach, Sabine s.griessbach@fz-juelich.de | JSC SimLab Climate Science | none | satellite data, infrared spectra, meteorological data | unsupervied ML |
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| Schober, Martin m.schober@fz-juelich.de | INM-1 Fiber Architecture Group | Beginner | Microscopic resolution 2D and 3D images, scalar and vector valued data | |
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| Gohlke, Holger h.gohlke@fz-juelich.de | JSC / ICS-6 / NIC Research Group | Deep learning / Supervised/unsupervised learning | Molecular structure data | meet ML experts, possibility to discuss individual DL problem |
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| Ungermann, Jörn j.ungermann@fz-juelich.de | IEK-7 | Inverse Modelling, Linear Regression | 2-D and 3-D interferograms | |
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| Nöh, Katharina k.noeh@fz-juelich.de | IBG-1: Biotechnology, Modeling and Simulation Group | Image segmentation with DL (U-Nets) | Large-scale microscopic images from time-lapse microscopy (2D, x-TB range) | Meet DL/ML experts, discuss DL solutions for specific applications | |
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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 |
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| Wenzel, Susanne s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | |
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|Zimmermann, Olav olav.zimmermann@fz-juelich.de | JSC Simlab Biology | Supervised learning (SVM and its variants), Dimension Reduction (Isomap et al, clustering), Metaheuristics, basics of sequence learning methods (CRF, LSTM), bioinformatics | biological sequence data, molecular structure data, experimental data (2-dim to n-dim or graphs), unstructured data | ML for hypertoroidal output spaces in depth understanding of seq2seq methods, representation of world knowledge for learning methods that work with non-differentiable loss | |