|
|
|
| Name (Last, First) | mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs | expire date (default 25.6.2019) |
|
|
|
|
|:---|:---|:---|:---|:---|:---|---:|
|
|
|
|
| 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 |
|
|
|
|
| Wenzel, Susanne | s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | | 14.02.2020 |
|
|
|
|
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 |
|
|
|
|
| Kraus, Jiri | jkraus@nvidia.com | NVIDIA | | | Want to learn the ML/DL needs of scientist at FZJ | none |
|
|
|
|
|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 | |
|
|
|
|
|Herten, Andreas | a.herten@fz-juelich.de | JSC NVIDIA Application Lab | Software installation | | Overview/support of ML/DL software in Jülich | |
|
|
|
|
| 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 |
|
|
|
|
|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 |
|
|
|
|
| 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 |
|
|
|
|
| Hagemeier, Björn | b.hagemeier@fz-juelich.de | JSC Project Helmholtz Analytics Framework | Basic ML methods | | | |
|
|
|
|
| Name (Last, First), mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs | expire date (default 25.6.2019) |
|
|
|
|
|:---|:---|:---|:---|:---|---:|
|
|
|
|
| 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 |
|
|
|
|
| Wenzel, Susanne s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | | 14.02.2020 |
|
|
|
|
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 |
|
|
|
|
| Kraus, Jiri jkraus@nvidia.com | NVIDIA | | | Want to learn the ML/DL needs of scientist at FZJ | none |
|
|
|
|
|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 | |
|
|
|
|
|Herten, Andreas a.herten@fz-juelich.de | JSC NVIDIA Application Lab | Software installation | | Overview/support of ML/DL software in Jülich | |
|
|
|
|
| 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 |
|
|
|
|
|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 |
|
|
|
|
| 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 |
|
|
|
|
| Hagemeier, Björn b.hagemeier@fz-juelich.de | JSC Project Helmholtz Analytics Framework | Basic ML methods | | | |
|
|
|
|
|
|
|
\ No newline at end of file |
