|
|
[[back](How to get in contact)] [[Home](Home)]
|
|
|
|
|
|
---
|
|
|
|
|
|
| Name (Last, First), mail | FZJ Institute/Group | Your ML expertise | Your type of data | Your needs |
|
|
|
|:---|:---|:---|:---|:---|
|
|
|
| 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 |
|
... | ... | @@ -33,3 +37,6 @@ |
|
|
| 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 |
|
|
|
| Wenzel, Susanne s.wenzel@fz-juelich.de | INM-1 Big Data Analytics Group (BDA) | Markov Marked Point Processes, rjMCMC | | |
|
|
|
|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 |
|
|
|
|
|
|
---
|
|
|
[[back](How to get in contact)] [[Home](Home)] |
|
|
\ No newline at end of file |