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| Name (Last, First) | mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs | expire date (default 25.6.2019) |
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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) | none |
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| Wenzel, Susanne | s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group | Markov Marked Point Processes, rjMCMC | | | 14.02.2020 |
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Dickscheid, Timo | t.dickscheid@fz-juelich.de | INM-1 Big Data Analytics Group | Deep Learning with ConvNets, Image Segmentation, Image Feature detection, Markov Random Fields, Clustering, Analogies to the human brain| Microscopic resolution 2D and 3D images | | | none | |
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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) | none |
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| Wenzel, Susanne | s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | | 14.02.2020 |
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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 | | | none |
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| Kraus, Jiri | jkraus@nvidia.com | NVIDIA | | | Want to learn the ML/DL needs of scientist at FZJ | none |
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|Zimmermann, Olav | olav.zimmermann@fz-juelich.de | JSC Simlab Biology | Supervised learning (SVM and its variants),
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Dimension Reduction (Isomap et al, clustering), Metaheuristics, basics of sequence learning methods (CRF, LSTM),
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bioinformatics | biological sequence data, molecular structure data, experimental data (2-dim to n-dim or graphs)
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unstructured data | ML for hypertoroidal output spaces in depth understanding of seq2seq methods
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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 | none | |
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