Deep Learning
Deep Learning
You can find an institute-wide list of all thesis topics that require deep learning methods and tools on this page. Links to the information of the corresponding supervising research groups are given. A list of "general" topics are given additionally. These are topics that focus on the development of novel deep learning techniques rather than specific applications in research groups. These topics can be worked on either within the framework of a research group or across multiple groups. All topics (and methods) are to be taken as suggestions and can be adapted if necessary. Details on the theses can be discussed with the respective supervisor. For a first impression, you will find a list of deep learning technique keywords attached to each topic. Please note that not all topics may be recommendable for deep learning beginners.
Abbreviations
NN: Neural Network
DNN: Deep Neural Network
FCN: Fully Connected Network
CNN: Convolutional Neural Network
GNN: Graph Neural Network
GCN: Graph Convolutional Network
BNN: Bayesian Neural Network
1D Conv.: 1 Dimensional Convolution
GAN: Generative Adverserial Network
Bachelor
EnEx/TRIPLE
Acoustic Signal Analysis with Machine Learning Methods
IceAct
Analysis of air-shower images with machine learning methods (Image recognition)
IceCube
Visualization and Analysis of Feature Importance for Deep Learning Reconstruction Methods (CNN/Feature Analysis)
Testing the robustness of deep learning reconstruction methods against adversarial attacks (Adverserial Attacks)
JUNO
Particle Identification using Deep Learning Methods (GCNs/AutoEncoder/Classic/Recurrent)
SiFi-CC
Improvement of event identification for the SiFi-CC with neural networks (Image recognition/CNNs/FCNs/BNN)
General
- Discrimination with AutoEncoder (e.g. Positron - Elektron Discrimination) (AutoEncoder)
- Test of a noval neural network algorithm: GNN based AutoEncoder (e.g. DoubleChooz) (AutoEncoder/GNNs)
Master
Einstein Telescope
Stuides on Gravity Gradient Noise Mitigation via Deep Learning. (Recurrent/Transformer/Attention/AutoEncoder/1D Conv.)
IceAct
Event reconstruction with Machine Learning (Random Forrest, Graph NN)
IceCube
Understanding hidden and correlated uncertainties and Deep Learning Methods in IceCube in the context of adversarial attacks (Adverserial Attacks)
Application of Recurrent Neural Networks for Analysing time series data in IceCube and application to magnetic monopole searches (Recurrent NNs)
Optimizing the DNN Data Selection for the measurement of astrophysical neutrino fluxes (DNN)
JUNO
Particle Identification using Deep Learning Methods (GCNs/AutoEncoder/Classic/Recurrent)
SiFi-CC
Improvement of event identification for the SiFi-CC with neural networks (Image recognition/CNNs/FCNs/BNN)
General
- Discrimination with AutoEncoder (e.g. Positron - Elektron Discrimination) (AutoEncoder)
- Feature importance visualization (Feature Analysis)
- Generative Adverserial Networks (z.B. DoubleChooz/IceCube/Einstein) (GANs (GCNs))