Goal: The iterative improvement of an LLM-based iOS application that acts as a personalized assistant in word search, object description and question answering and achieves speech therapy results by engaging users in chat or speech-based language interactions.

Basic principles: Cognitive decline increases with age, which can be seen in language fluency. Older people who struggle with word retrieval, language processing or memory lapses can become increasingly isolated and worried about their ability to communicate effectively. LLM-based assistants offer a promising complement to traditional speech therapy, with the potential for even greater impact through engagement-driven interactions. However, the success of this approach depends on the ability of older people to use the app effectively and independently.

Methods: We are conducting a feasibility study to assess the app's ability to engage older people in frequent, meaningful language interactions with measurable speech therapy outcomes. The results of the study will help the developers to iteratively improve the application and the underlying LLM.

We are seeking applicants for a computer science master's degree for the MHH DigiStructMed program (https://www.mhh.de/en/hbrs/digistrucmed/ueber-das-programm).

Your tasks:
* Improving the accuracy and personalization of the LLM using prompt optimization, RAG approaches, and/or possibly federated learning approaches.
* Development of novel, engaging speech interactions with speech therapy outcomes
* UI/UX development for an older user group with iterative improvements based on engagement metrics.

The tactile interpretation of images requires the transformation of two-dimensional visual information into three-dimensional representations accessible through touch. This conversion presents significant challenges, particularly in the preservation of crucial expressive elements that in visual reality are highlighted through light and shadow.

The project focuses on case studies of pictorial art; for example, works by Caravaggio, where chiaroscuro creates a true 'tactile map' through:
- Anatomies highlighted by shadows (muscles, ribs, veins)
- Movement-defining fabric folds
- Depth of environments
- Textures of objects (fruit, fabrics, metals)

The objective of this thesis is to develop an AI system that overcomes current limitations in the automatic conversion of images into tactile representations, focusing on:
- Recognition of elements that in visual reality are highlighted by light and shadow
- Translation of these elements into appropriate depth maps
- Preservation of key narrative elements
- Optimisation of high relief rendering
The project is being developed in parallel with tactile image production experts who will test the effectiveness of the developed solutions.

The project offers the opportunity to:
- Working on a concrete problem of cultural accessibility
- Developing an innovative image interpretation system
- Collaborating with experts in the field of tactile representations
- Contributing to innovation in art accessibility

The Else-Kröner Graduate Program "Digital Transformation in Medicine" (DigiStrucMed) is a collaborative initiative of Hannover Medical School, the Technical University of Braunschweig, the University of Applied Sciences and Arts Hanover, and Leibniz University Hanover. Its goal is to support interdisciplinary training for students in medicine (doctoral candidates) and computer science (Master’s students working on their theses). The structured program is funded with €900,000 for an additional three-year period by the Else Kröner-Fresenius Foundation, enabling students from both disciplines to conduct joint research in digital transformation in medicine through project tandems.

For the 5th cohort in the 2025/26 program year, DigiStrucMed medical students will begin on July 1 or August 1, 2025. Master’s students may start their projects, in coordination with project supervisors, between June 1, 2025, and February 1, 2026. Further information about DigiStrucMed can be found on the program homepage: https://www.mhh.de/hbrs/digistrucmed.

Al-supported evaluation of scientific publications about the influence of pre-analytical factors (like temperature or time) on biosamples (e.g. blood) as a basis for a knowledge database for pre-analytical variability and biosample quality (ProvideQ).

During the entire pre-analytical process chain from sample collection to storage, biomaterials are particularly vulnerable and sample quality can be massively impaired by unfavorable pre-analytical factors. For example, high or low temperatures and long storage or transportation times can change the molecular profile of the samples in such a way that these samples only reflect the original physiological state to a limited extent. Different pre-analytical factors have different effects on different classes of molecules (e.g. RNA, proteins or metabolites) and sometimes there are even different influences within different molecules of a molecular class.

Although the effect of various pre-analytical influencing factors on biosamples has already been examined in numerous studies, it is still associated with extensive literature research to obtain an overview of the influence of pre-analytical influencing factors on biosamples and, for example, to answer the question of the extent to which a biospecimen can still be used for certain analyses (fit for purpose).

This problem led to the development of a knowledge database (ProvideQ) based on scientific literature. ProvideQ maps pre-analytical influences to analytes, initially focusing on blood samples and their metabolites: users can use ProvideQ to check which pre-analytical factors affect the stability of the metabolites of a plasma or serum sample and receive literature references for their query as well as information on whether a metabolite or which metabolites can be classified as stable under the pre-analytical conditions entered or whether the concentration changes. The objective of this suggested Master project is to develop an AI that autonomously analyzes the given literature (pdf format), evaluates it, and translates the results into the corresponding fields in the database.

We are delighted to announce that we are now accepting applications for a Master’s thesis or Bachelor's thesis on the topic of "Improvements of Long-Term Time Series Forecasting with the Use of Neural Networks." This cutting-edge topic offers a compelling challenge and significant opportunities for contributing to the field of computer science and data analytics.

In this thesis, students will delve into advanced neural network architectures to tackle long-term time series forecasting challenges. The primary objective is to enhance the predictive capabilities beyond current methodologies, focusing on the scalability, efficiency, and accuracy of forecasting models. Candidates will research and potentially develop innovative algorithms that can handle the complexities of long-duration forecasts with higher precision.

Students interested in this topic should have strong academic background in computer science, solid understanding of machine learning, particularly neural networks. Interest in or experience with time series analysis is beneficial but not mandatory.

Possible start date for this thesis is 01.09.2024.

Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today’s robots. DIGIT [1] is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli.

In situations where the sensor is unavailable or experiment repetitions are costly, the value of a reliable, real-time simulation becomes evident. Such a simulation can effectively estimate sensor outputs for various touch scenarios. Such simulation would offer a good alternative to gathering data in different setups and environments.

While several studies have introduced simulations for the DIGIT sensor, such as TACTO [2] and Taxim [3], they predominantly rely on rigid-body simulations and overlook the crucial soft-body aspect of the sensor's gel tip. This oversight diminishes the accuracy of the simulations and their ability to faithfully replicate real-world tactile interactions.


Goals of the thesis

Collect real image outputs and simulated sensor surface deformation.
Train a machine learning algorithm to generate output images using DIGIT surface deformation mesh.
Perform manipulation/grasping tasks with a real UR5 robot to assess and evaluate the performance of the trained algorithm.

The human hand is often used as the gold standard or goal for robotic manipulation, and haptic perception and illustration are often used to illustrate sophisticated robots and AIs. However, despite our fascination for anthropomorphic robotic hands, the trend in industrial applications and robotic challenges is to use simpler designs consisting of parallel grippers or suction cups. This trend is not necessarily only due to the complexity of matching the human hand's dexterity, robustness and perceptual capabilities but also to the reality that anthropomorphic hands may not be required to achieve the human skill level. For instance, the winner of the Amazon Picking Challenge used an end effector based on a suction system. In the DARPA Robotics Challenge, 15 of 25 teams used an underactuated hand with three or four fingers (Piazza et al., 2019), while none of the remaining ten teams used a fully actuated anthropomorphic hand (Piazza et al., 2019). Even in the Cybathlon, the winner of the Powered Arm Prosthesis Race used a body-powered hook (Piazza et al., 2019).

In addition, the synergistic combination of all three subsystems, including the mechanical aspects, perception, and control, might be more critical than an anthropomorphic robotic hand to match and potentially surpass human dexterous manipulation capabilities.

All of which prompts the question of whether we need anthropomorphic robotic hands. This thesis aims to answer this question from a multi-objective optimization point of view.


Goals of the thesis

Implement a Genetic Algorithm or another multi-objective optimization algorithm to develop a general-purpose robotic manipulator. The manipulator could be based on the human hand, ideally reducing complexity while keeping most of its dexterity.

I would like to announce that we are now accepting applications for a Master's thesis, Bachelor's thesis on the topic of "Time series forecasting with the use of neural networks." This is an exciting and innovative area of study that has the potential to make significant contributions to the field of computer science.

In this thesis, students will have the opportunity to explore the use of neural networks for a time series forecasting problem that has traditionally been difficult to solve using traditional machine learning techniques. The goal of this research is to develop new algorithms and techniques that can accurately forecast time series data, with a focus on improving the performance and accuracy of existing methods.

Students interested in this topic should have a strong background in computer science, with a particular focus on machine learning and neural networks. Knowledge of time series analysis and regression techniques is also helpful but not required.

The world of neural networks, especially in the areas of image classification and time series processing, continues to expand rapidly. In this master's thesis project, we aim to explore the fusion of these two domains by experimenting with innovative combinations of deep learning, neural network architectures.

Students who are interested in delving deep into neural network architectures and unveiling efficient methods to merge them into a cohesive, high-performance system are encouraged to apply.
The learning materials are provided and you will have the opportunity to implement your own ideas.

The thesis steps include:

A comprehensive literature review on the topic
Undertaking exploratory data analysis of both images and time series
Engaging in feature engineering to enhance model performance
Signals modelling to understand data patterns
Evaluating model performance and interpreting results
Refining the model to achieve optimal outcomes
Documenting the entire process and creating a final report

This thesis aims to explore the application of OpenAI's ChatGPT in the domain of drones. The objective is to investigate the feasibility and effectiveness of leveraging ChatGPT for various drone tasks in both real and simulated environments. The thesis proposes a comprehensive strategy that combines prompt engineering principles and software engineering skills to facilitate ChatGPT's adaptation to different drone tasks.

The study will evaluate the efficacy of different prompt engineering techniques, dialog strategies, and software architectural decisions in the context of executing drone-related tasks. Special emphasis will be placed on ChatGPT's capabilities in utilizing free-form dialogue, code synthesis, task-specific prompting functions, and closed-loop reasoning through dialogues.

The thesis will cover a wide range of drone tasks, including aerial navigation, object detection, and path planning. The primary objective is to ascertain whether ChatGPT can effectively solve these tasks while enabling users to interact predominantly through natural language instructions.

Throughout the research, both real-world drone experiments and simulated scenarios will be conducted to assess the performance and adaptability of ChatGPT. Software engineering best practices will be employed to ensure the robustness and scalability of the system.

The anticipated outcome of this thesis is to demonstrate the potential of ChatGPT as a valuable tool for drone applications, showcasing its ability to enhance user interactions and task execution through natural language instructions.

Keywords: ChatGPT, drones, prompt engineering, natural language interaction, aerial navigation, object detection, path planning, simulated environment, software architecture.

Although camera technology has developed considerably in the consumer
market, most robots still use inexpensive webcams (or RGB-D cameras),
which cannot deal with challenging lighting conditions and lack optical
image stabilization, which significantly impacts the quality of the
images generated. Additionally, image and videos "transport" packages
(e.g. https://wiki.ros.org/image_transport_plugins) use very dated
technologies with a detrimental effect on band-wide usage and latency in
robot applications.

Possible goals of the thesis
* Implement an Open Source ROS(2) package with improved image compression
* Implement an Open Source ROS(2) package with high-dynamic range (HDR)
capabilities

The user should be able to enable one or both at a time. The compression
and HDR levels should be adjusted from 0 to a maximum to be defined.

Haptic sensors span a broad range of technologies. The main focus of the
sensors is to increase the recognition accuracy of both textures and the
location of contact points. However, these sensors are mechanically
fragile and mounted externally to robotic systems to increase accuracy,
limiting the use of those sensors to applications that are kind to the
sensors. For use in harsh applications or complementary to those
existing sensors, this project aims to develop a machine
learning-oriented solution capable of using body-borne vibrations to
classify objects' texture and location of haptic interaction. This
strategy allows mounting the sensors inside the robot, protected from
external perturbance. Although this technology is not as accurate as
other technologies, it promises to enable a degree of haptic perception
anywhere the robot's outer shell (and electronics) can withstand.
The technology has been validated in applications of multimodal object
recognition, e.g., by Bonner et al. 2021 and Toprak et al. 2018. Some of
the following steps include the development of the algorithms to perform
localization of multiple points of contact between the robot and
external objects.

Goal
* Systematic collect sound and vibration data from a robotic hand
* Determine ideal placement of sensors
* Perform sound-source localization of the source of the vibration or
sound within the robotic hand

Multimodal object recognition is still an emerging and active field of research. Haptic sensors span a broad range of technologies. The main focus of the sensors is to increase the recognition accuracy of both textures and the location of contact points. However, these sensors are mechanically fragile and mounted externally to robotic systems to increase accuracy, limiting the use of those sensors to applications that are kind to the sensors. For use in harsh applications or complementary to those existing sensors, this project aims to develop a machine learning oriented solution capable of using body-borne
vibrations to classify objects' texture and location of haptic interaction. This strategy allows mounting the sensors inside the robot, protected from external perturbance. Although this technology is not as accurate as other technologies, it promises to enable a degree of haptic perception anywhere the robot outer shell (and electronics) can withstand. The technology has been validated in applications of multimodal object recognition, e.g., by Bonner et al. 2021 and Toprak et al. 2018. Some of the following steps include 1) the development of "robotic fingerprints" that maximize the body-borne vibrations, 2) and later developing the algorithms to perform localization of multiple points of contact between the robot and external objects.

Goal
* development of 3D printed robotic fingerprints for robotic grippers.
- systematic optimization of fingerprint's texture
- systematic selection of material for different applications, underwater manipulation, humanoid robots, etc.
- creation of a haptic dataset

As shown in multiple research, providing feedback to autonomous learning agents can speed up learning. However, the quantification/characterization of different aspects of feedback, such as feedback quantity, quality, temporal and spatial misalignments, etc., in learning speed, performance, and other relevant metrics is still an open question. This question not only addresses theoretical aspects of the learning algorithms but is also very relevant for application in real systems because although feedback might be beneficial, (human-)feedback is also expensive and adds complexity to the systems. Thus, it is essential to know the minimal requirements for the (human-)feedback to achieve a significant increment in performance, learning speed etc., that it is worth the added complexity. Hence, this project presents a series of questions that can be addressed independently to achieve a deeper understanding of the role of feedback in a learning system's performance.

Several assumptions and simplifications can be made to facilitate the study of these questions. These include the use of binary and low-dimensional feedback, simulated environments, and the use of autonomous teachers. Moreover, this project will be studied in a robot reaching task for a KUKA LBR iiwa, a robotic arm with 7 degrees of freedom (DoF). Configurations from 1 to 7 DoF will be used to study feedback effects at different levels of task complexity.

This project will use artificial feedback and primarily be studied in simulated environments. Eventually, once a better understanding of the effects of feedback is obtained, experiments with real users will be carried out. Several thesis directions are possible, which will be discussed with the candidates. These include:

- Quantifying the Effect of Feedback Accuracy in IRL Performance
- Quantifying the Effect of Feedback Quantity in IRL Performance (e.g., binary, scalar value, or vector)
- Quantifying the Effect of Feedback Budget in Interactive Reinforcement Learning (e.g., early, uniform, late)
- Quantifying the Effect of Time-Delayed Feedback in IRL Performance
- Policy Shaping in IRL for Dynamical System using Binary and Other Low-Dimensional Feedback

Semantic Table Interpretation (STI) is the task of understanding the concepts in a table. This includes (i) cell-entity linking where objects mentioned in table cells are linked to resources such as Wikipedia, (ii) column-type annotation where the type of objects in a column is identified and (iii) column-property annotation which is about finding the relation between two columns [1]. One approach to perform these three tasks is the use of graph neural networks (GNNs) where the table, its columns, rows and cells are represented as a graph [2]. By training on a large corpus, the GNN performs the three tasks through node and edge classification [3].

The goal in this thesis is to develop a GNN that performs the three tasks jointly and to demonstrate that this joint training leads to more accurate annotations than when performing these tasks in isolation.

As a basis of this master thesis, we already have a method for table-to-graph conversion, a basic GNN for STI and an evaluation pipeline setup which will need to be extended to demonstrate the effect of joint training and its superiority over existing approaches.

Your goals are as follows:
- Understand STI and GNNs
- Extend an GNN (implemented in PyTorch Geometric) for STI
- Conduct experiments to evaluate the effectiveness of the GNN