Updated list of theses
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@ -13,11 +13,12 @@ looks in practice? The following documents have been typeset with this
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- S. Almasian, [Learning Joint Vector Representations of Words and Named Entities](https://github.com/satya77/Thesis_Entity_Embeddings/blob/master/MasterThesis_SatyaAlmasian.pdf), M.Sc. thesis, Heidelberg University, 2018
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- S. Almasian, [Learning Joint Vector Representations of Words and Named Entities](https://github.com/satya77/Thesis_Entity_Embeddings/blob/master/MasterThesis_SatyaAlmasian.pdf), M.Sc. thesis, Heidelberg University, 2018
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- E. Angriman, [Scalable Algorithms for the Analysis of Massive Networks](https://edoc.hu-berlin.de/handle/18452/25013), Ph.D. thesis, Humboldt University of Berlin, 2022
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- C. Bock, [Motifs and Manifolds: Statistical and Topological Machine Learning for Characterising and Classifying Biomedical Time Series](https://www.research-collection.ethz.ch/handle/20.500.11850/524042), Ph.D. thesis, ETH Zurich, 2021
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- K. Hanser, [Visualization of Coherence in Meteorological Data](https://github.com/hanserK/master_thesis/blob/master/Thesis_Karsten_Hanser.pdf), M.Sc. thesis, Heidelberg University, 2018
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- K. Hanser, [Visualization of Coherence in Meteorological Data](https://github.com/hanserK/master_thesis/blob/master/Thesis_Karsten_Hanser.pdf), M.Sc. thesis, Heidelberg University, 2018
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- M. Moor, [Machine Learning on Clinical Time Series: Classification and Representation
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- M. Moor, [Machine Learning on Clinical Time Series: Classification and Representation
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Learning](https://www.research-collection.ethz.ch/handle/20.500.11850/532377), Ph.D. thesis, ETH Zurich, 2022
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Learning](https://www.research-collection.ethz.ch/handle/20.500.11850/532377), Ph.D. thesis, ETH Zurich, 2022
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- B. Rieck, [Persistent Homology in Multivariate Data Visualization](http://archiv.ub.uni-heidelberg.de/volltextserver/22914/1/Dissertation.pdf), Ph.D. thesis, Heidelberg University, 2017
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- B. Rieck, [Persistent Homology in Multivariate Data Visualization](http://archiv.ub.uni-heidelberg.de/volltextserver/22914/1/Dissertation.pdf), Ph.D. thesis, Heidelberg University, 2017
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- E. Angriman, [Scalable Algorithms for the Analysis of Massive Networks](https://edoc.hu-berlin.de/handle/18452/25013), Ph.D. thesis, Humboldt University of Berlin, 2022
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Please open a pull request if you want your document to be listed here,
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Please open a pull request if you want your document to be listed here,
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and consider acknowledging this repository.
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and consider acknowledging this repository.
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