Thesis Topics

This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. ‘machine learning’ or others.

PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a simple CV, grade sheet, and topic ideas (if any). We will answer shortly.

Of course, your own ideas are always welcome!

Machine Learning-based Surrogate Models for Accelerated Flow Simulations

Type of Work:

  • Master

Keywords:

  • Machine Learning
  • Microstructure Property Prediction
  • Surrogate Modeling

Description:

Surrogate modeling involves creating a simplified and computationally efficient machine learning model that approximates the behavior of a complex system, enabling faster predictions and analysis. For complex systems such as fluids, their behavior is governed by partial differential equations. By solving these PDEs, one can predict how a fluid behaves in a specific environment and conditions. The computational time and resources needed to solve a PDE system depend on the size of the fluid domain and the complexity of the PDE. In practical applications where multiple environments and conditions are to be studied, it becomes very expensive to generate many solutions to such PDEs. Here, modern machine learning or deep learning-based surrogate models which offer fast inference times in the online phase are of interest.

In this work, the focus will be on developing surrogate models to replace the flow simulations in fiber-reinforced composite materials governed by the Navier-Stokes equation. Using a conventional PDE solver, a dataset of reference solutions was generated for supervised learning. In this thesis, your tasks will include the conceptualization and implementation of different ML architectures suited for this task, training and evaluation of the models on the available dataset. You will start with simple fully connected architectures and later extend it to 3D convolutional architectures. Also of interest is the infusion of the available domain knowledge into the ML models, known as physics-informed machine learning.

By applying ML to fluid applications, you will learn to acquire the right amount of domain specific knowledge and analyze your results together with domain experts from the field.

If you are interested, please send me an email with your Curriculum Vitae (CV), your Transcript of records and a short statement about your background in related topics.

References:

  • Santos, J.E., Xu, D., Jo, H., Landry, C.J., Prodanović, M., Pyrcz, M.J., 2020. PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media. Advances in Water Resources 138, 103539. https://doi.org/10.1016/j.advwatres.2020.103539
  • Kashefi, A., Mukerji, T., 2021. Point-cloud deep learning of porous media for permeability prediction. Physics of Fluids 33, 097109. https://doi.org/10.1063/5.0063904

Segmentation of Shoe Trace Images

Type of Work:

  • Master

Keywords:

  • benchmarking
  • deep learning
  • image segmentation
  • keypoint extraction
  • self-attention

Description:

Help fight crime with AI! The DFKI and the Artificial Intelligence Transferlab of the State Criminal Police Office (Landskriminalamt) are searching for master candidates eager to apply their knowledge in AI to support crime scene analysis. The student will have the opportunity to visit the Transferlab in Mainz for an in-depth introduction to the topic and full access to DFKI’s computing cluster infrastructure.

General goal: improve identification of specific markers normally present in shoe trace images acquired in crime scenes.

Specific goals:

  1. [benchmarking] evaluate existing image segmentation models in the context of shoe trace analysis;
  2. [research] propose a segmentation model combining semantics and keypoint information tailored to specific markers present in crime scene photographs;
  3. [research] assess model performance on labeled data.
  4. [research] definition of limits and requirements for the existing training- and test-data.

Retrieval of Shoe Sole Images

Type of Work:

  • Master

Keywords:

  • benchmarking
  • deep learning
  • graph neural networks
  • image retrieval
  • keypoint extraction

Description:

Help fight crime with AI! The DFKI and the Artificial Intelligence Transferlab of the State Criminal Police Office (Landskriminalamt) are searching for master candidates eager to apply their knowledge in AI to support crime scene analysis. The student will have the opportunity to visit the Transferlab in Mainz for an in-depth introduction to the topic and full access to DFKI’s computing cluster infrastructure.

General goal: improve retrieval of shoe sole images acquired in laboratory, i.e. under controlled conditions and used as reference by forensics specialists.

Specific goals:

  1. [benchmarking] evaluate existing image retrieval approaches in the context of shoe trace recognition;
  2. [research] propose a graph network architecture based on keypoint information extracted from the images.
  3. [research] evaluate performance of proposed model against existing methods.

Sherlock Holmes goes AI - Generative comics art of detective scenes and identikits

Type of Work:

  • Master

Keywords:

  • Bias in image generation models
  • Deep Learning Frameworks
  • Frontend visualization
  • Speech-To-Text, Text-to-Image Models
  • Transformers, Diffusion Models, Hugging Face

Description:

Sherlock Holmes is taking the statement of the witness. The witness is describing the appearance of the perpetrator and the forensic setting they still remember. Your task as the AI investigator will be to generate a comic sketch of the scene and phantom images of the accused person based on the spoken statement of the witness. For this you will use state-of-the-art transformers and visualize the output in an application. As AI investigator you will detect, qualify and quantify bias in the images which are produced by different generation models you have chosen.

Note:

This work is embedded in the DFKI KI4Pol lab together with the law enforcement agencies. The stories are fictional you will not work on true crime.

Requirements:

  • German level B1/2 or equivalent
  • Outstanding academic achievements
  • Motivational cover letter

Generative Adversarial Networks for Agricultural Yield Prediction

Type of Work:

  • Guided Research
  • Master

Keywords:

  • Deep Learning
  • Generative Adversarial Networks
  • Yield Prediction

Description:

Agricultural yield prediction has been an essential research area for many
years, as it helps farmers and policymakers to make informed decisions about
crop management, resource allocation, and food security. Computer vision and machine learning techniques have shown promising results in predicting crop
yield, but there is still room for improvement in the accuracy and precision
of these predictions. Generative Adversarial Networks (GANs) are a type of neural network that has shown success in generating realistic images, which can be leveraged for the prediction of agricultural yields.

References:

  • ‘Goodfellow, Ian, et al. “Generative adversarial networks.” Communications of the ACM 63.11 (2020)': 139-144.
  • ‘Z. Xu, J. Du, J. Wang, C. Jiang and Y. Ren, “Satellite Image Prediction Relying on GAN and LSTM Neural Networks,” ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1-6, doi’: 10.1109/ICC.2019.8761462.
  • ‘Drees, Lukas, et al. “Temporal prediction and evaluation of brassica growth in the field using conditional generative adversarial networks.” Computers and Electronics in Agriculture 190 (2021)': 106415

Efficient Sampling of Training Patches for Image Super-Resolution

Type of Work:

  • Guided Research
  • Master

Keywords:

  • benchmark
  • deep learning
  • proxy datasets
  • single image super-resolution

Description:

Typically, the Single Image Super-Resolution (SISR) training pipeline decomposes train images into sub-images. It follows the deep learning paradigm of “more data is better.” Be that as it may, it is not necessarily true [1, 2]. This work aims to rethink the image decomposition for the SISR training pipeline and test various sampling methods. The results should be evaluated using state-of-the-art deep learning models on standard datasets.

Knowledge Graphs für das Immobilienmanagement

Type of Work:

  • Bachelor
  • Master

Keywords:

  • corporate memory
  • knowledge graph
  • ontologie

Description:

Das Management von Immobilien ist komplex und umfasst verschiedenste Informationsquellen und -objekte zur Durchführung der Prozesse. Ein Corporate Memory kann hier unterstützen in der Analyse und Abbildung des Informationsraums um Wissensdienste zu ermöglichen. Aufgabe ist es, eine Ontologie für das Immobilienmanagement zu entwerfen und beispielhaft ein Szenario zu entwickeln. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.

Graph Structure and Node Label Prediction on Circuit Diagrams

Type of Work:

  • Master

Keywords:

  • Autoencoder
  • GAN
  • Graph Neural Network
  • LSTM

Description:

The objective of this master’s thesis is to examine the performance of deep (graph) neural networks for node labeling and structure prediction on graph structures representing electrical circuit diagrams. In the course of the thesis, the following model types should be evaluated: Classification Models should be used to aid the human user to get insights in the circuit’s functional principle. For example, all nodes of a circuit should be classified whether or not they contribute to the power supply of the described device. This part of the thesis is intended to compete (partly) against the existing rule-based approach. Transformative Models should be trained in a natural-language translator-like fashion to map one circuit to another, in which the entire graph structure is mapped to a coding space by an encode an subsequently unwrapped by a decoder model. The output graph should be an alternate version of the input graph which desirable properties. For example, the output graph should represent an energy- optimized version of the input device. Optionally, the process of turning the content of a coding space into a graph structure should be examined here. Biologically inspired approaches should be considered in which a single node divides, and differentiates under the exchange of signals like cells in embryonic tissue (mitosis, apoptosis, release and reception of cytokines). Generative Models (trained e.g. in GAN structures) should allow for the high-dimension interpolation between graphs. This should facilitate the generation of new devices like “a mixture of an AM and an FM radio”, “a slightly more toaster-ish backing oven.”

Fault and Efficiency Prediction in High Performance Computing

Type of Work:

  • Master Thesis

Keywords:

  • deep learning
  • event data modelling
  • survival modelling
  • time series

Description:

High use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. This project studies log data of the DFKI Kaiserslautern high performance cluster to consider the predictability of adverse events (node failure, GPU freeze), energy usage and identify the most relevant data within. The second supervisor for this work is Joachim Folz.

Data is available via Prometheus-compatible system:

Reference:

Feel free to reach out if the topic sounds interesting or if you have ideas related to this work. We can then brainstorm a specific research question together. Link to my personal website.

Construction & Application of Enterprise Knowledge Graphs in the E-Invoicing Domain

Type of Work:

  • Bachelor
  • Guided Research Project
  • Master

Keywords:

  • knowledge graphs
  • knowledge services
  • linked data
  • semantic web

Description:

In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.

Graph Extraction/Generation from Diagrams

Type of Work:

  • Bachelor
  • Master
  • Project
  • Seminar

Keywords:

  • cnn
  • cv
  • dynamic system simulation

Description:

I am currently supervising students in multiple topics related to the extraction and simulation of Graph-Based Engineering Drawings from optical sources: Graph Extraction from Printed/Handdrawn Circuit Diagrams/Piping&Instumentation Diagrams, Generation of Hand-Drawn Diagrams, Interactive Circuit Detection and Simulation

Learning Analytics in Education

Type of Work:

  • Master
  • Project

Keywords:

  • affective state
  • cognitive state
  • deep learning
  • machine learning

Description:

None

Anomaly detection in time-series

Type of Work:

  • Master
  • Project

Keywords:

  • cnn
  • explainability

Description:

Working on deep neural networks for making the time-series anomaly detection process more robust. An important aspect of this process is explainability of the decision taken by a network.

Time Series Forecasting Using transformer Networks

Type of Work:

  • Guided Research
  • Project

Keywords:

  • time series forecasting
  • transformer networks

Description:

Transformer networks have emerged as competent architecture for modeling sequences. This research will primarily focus on using transformer networks for forecasting time series (multivariate/ univariate) and may also involve fusing knowledge into the machine learning architecture.

Previous Job Offers
Next Contact