Thesis

Options

There are mainly three options for the thesis:

  1. Choose a topic, or propose an own topic and work on your own (see list below for options) (this is the default option)
  2. Collaboration with local start-ups, or companies (paid Master’s thesis)
  3. Work together with a research partner organisation

Template

Feel free to use the Latex thesis template (based on input from Karl Voit and Keith Andrews):

Template, and preview

Collection of a few helpful tips for Master’s thesis, provided by Annemarie Harzl. For printing the thesis one can choose the CopyShop or an online service (e.g., masterprint). After the thesis is finished beware of predatory publishers that offer to print the thesis for free!

Topics

Development of a Data-driven Injector Model for Condition Monitoring and Advanced Control Concepts The target of this thesis is to develop a data-driven and real-time capable injector model that predicts key injection parameters as a function of other engine parameters for an unworn and fully functional injector. (Details)

Development of a Data-Driven Injection Rate Model Based on Measurements With an Instrumented Diesel Fuel Injector The target of this thesis is to develop a data-driven model that predicts the fuel injection rate curve as a function of other signals obtained from an instrumented prototype injector. Injection rate measurements were carried out on a hydraulic test rig to generate a measurement database for modeling. (Details)

Development of a Data-driven Damage Model for Large Engine On-board In-cylinder Pressure Transducers The target of this thesis is to develop a data-driven damage model for a specific in-cylinder pressure transducer based on measurement data from dedicated durability tests that were carried out on a large high-speed single-cylinder research engine (SCE) at the Large Engines Competence Center (LEC). (Details)

Is MC Dropout Sensitive to Dead Neurons Please have a look at the Description

NLP

Information Extraction from Historical Text The 19th century is known for its big changes in politics, technology and society - and also called long nineteenth century. The goal of the thesis is to use NLP to support historians in their work to better understand events including their causes and how they have been perceived. To this end, sources like historical news papers are to be collected, and information extraction methods should be applied. In particular, the extraction of cause and effects related to historical events are of interest.

Recursive Word Sense Induction Collect a textual dataset, split and pre-process the data. Each word is then clustered, and pure clustered are used to split the word and replace its occurrence with a cluster representation. Continue this process until no pure clusters can be found.

Change in Authorship Style The writing style is unique to a person, but it is also subject to change. For example, if a person is exposed to a certain situation, the writing style might also change. There are a number of sub-topics here, including real-world experiments and dataset collection & analysis.

Causal Inference in NLP Measure the strength of causal effect via textual resources. How much does an event change the way people write about a topic? The event here could be a governmental intervention, a natural disaster, an accident, a personal experience.

Extraction of Causal Patterns for Knowledge Base Completion Extract causal knowledge from a specific domain and transform the extracted information in structured form. The goal is to build (or extend) a knowledge graph.

Zero-Shot Learning Recently, pre-trained models have been studied for their ability to solve problems without an explicit training phase. For example, just given some examples, a model can be adapted for sentiment detection or information extraction. As as starting point OpenPrompt can be used.

Learn to Stem Goal: Find out, which words should be stemmed, and which should remain intact in a preprocessing pipeline. Approach: Define a set of criteria (hypothesis) for making such a decision. Then, devise a evaluation design to find out, what the best criteria (or combination) is.

Wikipedia Article Generator Given just the infobox of an Wikipedia article, generate a full Wikipedia article.

Chatbot Goal: Develop an open-domain chatbot based on either GPT-2/3 or the Facebook Blender.

Data Science

Causal Outlier/Anomaly Detection Goals: 1) Given a dataset (including potentially unlabelled outliers) and a causal structure, research to which extend does the knowledge of the causal structure help to identify outliers. 2) Given a dataset and labelled outliers, research to which extend this helps for causal discovery.

High-Dimensional Poincaré Plot for Time Series Goal: Develop tools to assess the usefulness of longer ranging dependencies in time series data (i.e., instead of using consecutive observations, increase the distance for every dimension). Might also include window based approaches.

Software Development

Web App for Causal Exploration Goal: Extend an existing web-app consisting of three parts: 1) a part, where one can draw a simple causal graph, 2) a part, where one can upload and view a simple data-set (e.g., upload via .csv file), 3) an results part (e.g., an unbiased estimate of dependencies). Depending on the causal graph, the results section will be updated.

Deep Learning

Custom Loss for Privacy-Protection Develop a loss function when training e.g. a Variational Autoencoder to additionally include a loss term for the “leak” of sensitive information.

Speed-up symbolic regression with deep learning methods Use a deep learning model to restrict the search space of evolutionary algorithms (looks like sine-wave)