Fundamental or practice-oriented: we offer diverse and exciting topics for theses.
Are you looking for a topic for your bachelor or master thesis? Would you like to work on exciting projects during your studies?
We offer you many possibilities!
We offer thesis topics on the latest research or in cooperation with industry. The topics are:
- Applied Artificial Intelligence in Industries
- Semantic Data Management
- Industrial Natural Language Processing
- Transparent and Interpretable Artificial Intelligence
- Industrial Transfer Learning
- Sensory Time Series Data Analytics
- and much more!
Get in touch with us! You are also welcome to take the initiative: meisen[at]uni-wuppertal.de
Open Bachelor theses
Industrial Deep Learning
- Post-implementation of a reinforcement learning approach for production planning(PDF)
- Data set generation in a real environment using industrial robots(PDF)
- Reinforcement Learning for Product Scheduling - Flexible
Job Shop Scheduling with constraints(PDF) - Reinforcement Learning for Product Scheduling - Flexible
Job Shop Scheduling with multi-criteria orientation(PDF) - Design rules for agents in emergent language exploration(PDF)
- Dynamic Network Design for Continuous Learning(PDF)
- Interpretability and Transparency for Vision Transformer Models(PDF)
Virtual and Augmented Reality
- Examining the potential of virtual reality as a new teaching and learning medium(PDF)
- Conducting an exploratory study to determine various parameters for text in VR(PDF)
- Influence of abstraction in virtual reality on learning success(PDF)
- Research, categorisation and implementation of various teaching and learning methods for different contexts in virtual reality (PDF)
Digital transformation
Open Master's theses
Digital Transformation
- Implementation of digital twins in the industry(PDF)
- Monetising data with the help of the digital twin(PDF)
Industrial Deep Learning
- Texture generation with deep learning(PDF)
- Action sampling methods to increase the efficiency of trained deep learning models for production planning(PDF)
- Effective Deep Learning Architectures for Production Planning(PDF)
- Interpretability and transparency for Vision Transformer models(PDF)
- Explainable AI (XAI) for multi-camera based 3D object recognition(PDF)
- Hierarchical Reinforcement Learning Environment Design for Emergent Language(PDF)
- Quantification of pragmatic message processing - the next step towards 'Positive Listening'(PDF)
Semantic Systems Engineering
- The path to automated inspiration: Reverse engineering of image prompts in AI generation(PDF)
- The use of web crawlers for automated data data aggregation and quality monitoring in Dataspaces: Potentials, challenges and implementation
implementation strategies(PDF)
Virtual and Augmented Reality
- User-based evaluation of various types of virtual reality interaction in different application contexts(PDF)
- Investigation of different types of locomotion in virtual reality in relation to the application context(PDF)
- Where is the function? - Investigation of various concepts for menus in virtual reality(PDF)
Ongoing thesis
| Year | Type | Title | Student |
| 2024 | MA | Efficient use of neural agents for the job store scheduling problem through model compression | von Faber, Richard |
| 2024 | BA | Loading analysis of freight wagons using the Segment Anything model | Seewald, Samuel |
| 2024 | BA | Large Language Models for Code Development: A Systematic Literature Review of Current Approaches and Research Priorities | Aydin, Bugra |
Completed thesis
| Year | Type | Title | Student |
| 2023 | BA | Machine Learning Approach to Determine Post-View-Website-Visit Conversions of CTV Campaign Users for the Measurement of Advertising Campaigns | Jeyhun Hasanl |
| 2023 | MA | Machine learning method for predicting inspection interval parameters in manufacturing quality management | Simon Zürn |
| 2023 | BA | Effects of Adversarial State Perturbations on Emergent Language in Multi-Agent Reinforcement Learning | Osaze Obahor |
| 2023 | BA | Effective Curriculum Learning Strategies for Deep Reinforcement Learning on the Job Shop Scheduling Problem | Elias Theis |
| 2023 | MA | Evaluation of Monte Carlo Tree Search as a Policy Improvement Operator for Reinforcement Learning Based Job Shop Scheduling | Till Lemmer |
| 2023 | MA | Investigation of XAI methods for quality prediction in arc welding using discrete representations of a vector quantised variational autoencoder | Antonin Königsfeld |
| 2023 | BA | Curricular Reinforcement Learning for the Dual-Resource Constrained Job Shop Scheduling Problem | Max André Montag |
| 2023 | MA | Decision Transformers for Solving Production Scheduling Problems via Reinforcement Learning | Fabian Wolz |
| 2023 | MA | Requirement analysis and specification for data processing within functions of an IoT platform for energy monitoring of retail chains. | Jannik Bals |
| 2023 | BA | Search Algorithms for the Job Shop Scheduling Problem Leveraging Trained Deep Reinforcement Learning Agents | Paul Laszig |
| 2023 | MA | Neuroscience-inspired Ablation Studies for Vision Transformer | Florian Hölken |
| 2023 | BA | A Study of the Transferability of Customer Churn Representation Approaches on Other E-Commerce Use Cases | Ngoc Quynh Nhu Nguyen |
| 2023 | MA | Automatization of image dataset generation using an industrial roboter and further analysis regarding its reproducibility | Leon Wengenroth |
| 2023 | MA | Pose Estimation using Deep Learning and Systematic Dataset Generation for Industrial Manufacturing. | Ali Rida Bahja |
| 2023 | MA | A Comparison of Customer Representation Approaches in E-Commerce | Fahd Bouyaouzane |
| 2023 | MA | Automatic texture extraction on synthetic images using 6D Pose Estimation | Oliver Jan Jarosik |
| 2022 | MA | Implementation and Evaluation of Representation Learning Approaches for Quality Classification of Arc Welding | Mauritius Schulz |
| 2022 | MA | Conceptualization, development and evaluation of a web-based demonstrator for the addressee-oriented conveyance of the methods and results of deep reinforcement learning based production scheduling | Mohammad Malmir |
| 2022 | MA | Investigation of multi-task transformer models for visual inspection of freight wagons | Robin Teubert |
| 2022 | BA | Literature analysis on the state-of-the-art of production scheduling using reinforcement learning. | Mustafa Aydin |
| 2022 | BA | Investigation of time constraints for quality prediction in arc welding using deep learning. | Lars Thun |
| 2022 | BA | A Study of the Transferability of Machine Learning Methods on Different E-Commerce Forecasting Tasks. | Josias Schelkes |
| 2022 | MA | Self-Supervised Pre-Training for Long-Term Time Series Forecasting | David Stöter |
| 2022 | MA | Considering Time in the Creation of Activity Embeddings in Online Shopping Sessions | Mark Wönkhaus |
| 2022 | MA | Deep Reinforcement Learning for Job Shop Scheduling: Extension of a Python based Simulation Framework to include Transport and Retooling Times | Richard von Faber |
| 2022 | BA | Cross Robot Imitation Learning through Behavior Cloning for Industrial Assembly | Ilyes Rabai |
| 2022 | MA | Improve Deep Learning-Based Recommender Systems by Learning Customer Preferences | Saad Sebti |
| 2022 | BA | Untersuchung ortsabhängiger Einflüsse auf Deep Learning Modelle bei der Vorhersage von Flusspegeln anhand realer Daten des Flusses Wupper | Finn Lucas Elbl |
| 2022 | BA | Investigation of the generalization ability of a reinforcement learning agent for production scheduling | Merlin Montag |
| 2022 | BA | Untersuchung der Generalisierungsfähigkeit eines Reinforcement Learning Agenten durch Permutationen in einem Flexible Job Shop Scheduling Problem | Jan Voets |
| 2022 | MA | Evaluation and Comparison of Few-Shot Learning based Link Prediction Methods | Rebecca Braken |
| 2022 | MA | A Study of Recent Deep Learning-Based Recommender Systems by Evaluating Their Performance on Publicly Available Benchmark Datasets | Shady Yehia |
| 2022 | BA | An examination of the application of AI paradigms Continual Learning and Meta Learning in the industry sector of industrial manufacturing | Robin Gansäuer |
| 2022 | MA | Optimizing the storage location assignment in a high-bay warehouse with reinforcement learning. | Dimitri Tegomo Nanfack |
| 2022 | BA | The digital future of the craft sector - Construction of a knowledge base for the use of IoT sensor technology in the craft sector | Lena Schuster |
| 2022 | BA | Development of a Smart and Interactive Gantt-Chart for Assisted Production Planning | Georg Wanja Zemke |
