THA_connect is a project by Technical University of Applied Sciences Augsburg that supports international students from the beginning of their studies to their entry into the German job market. Through targeted programs, personalized advising, and a strong network, we help you succeed in your academic journey and take confident steps into your professional future.

THA_connect focuses on four core goals:

1. Ensuring academic success
    We support international students in successfully completing their degree programs.

2. Preparing for the job market
    We enhance your employability with hands-on, practice-oriented opportunities.

3. Facilitating transitions
    We build bridges between university and the labor market through tailored support services.

4. Building networks
    Through partnerships with companies, alumni, and institutions, we create lasting,
    sustainable structures for your future.

Detex 2.0 leverages artificial intelligence and high-resolution cameras to aid the sorting process in textile recycling. By optimizing the classification of clothing items and enhancing contamination detection, it enables more efficient processing of used textiles. →

Detex 2.0 leverages artificial intelligence and high-resolution cameras to aid the sorting process in textile recycling. By optimizing the classification of clothing items and enhancing contamination detection, it enables more efficient processing of used textiles. →

In collaboration with XITASO, we have developed a trade fair showcase that presents object recognition and segmentation using neural networks. By using RGB and infrared cameras, road users such as cyclists and pedestrians are recognised under different environmental conditions. Pre-trained models such as YOLOv8 and Mask R-CNN are used, which have been subsequently fine-tuned to infrared data to enable reliable detection even in poor lighting conditions. →

Employees at swa Netze lack an overview of the stock of usable spare parts and emergency materials. A tool for cross-departmental inventory management would be desirable, with which material stocks can be consolidated and sustainably reused in order to save money and resources. →

A machine learning application predicts patient volumes and thus facilitates nursing staff scheduling in order to efficiently manage staff shortages.

In cooperation with XITASO GmbH, an application was developed that uses machine learning to predict and visualise patient volumes in order to improve nursing staff scheduling. Based on historical data, the time series forecasting method is used, whereby models such as Random Forest, SARIMA and Holts Winters seasonal method are utilised. The model quality is evaluated by RMSE, MAPE and MAE. The predictions are presented in a user-friendly web interface, which enables care facilities to optimise staff planning in line with demand. →

Following the development of the prototype in the summer semester of 2023, the Pyramid app has now been officially published in the app stores. The app modernises communication between students and companies at Augsburg University of Applied Sciences' Pyramid Fair by supplementing existing sources of information such as the website, print and PDF catalogues. →

The increasing demand for components with tailored properties and functionally graded materials featuring adjustable interfaces is raising the requirements in gravity sand and die casting processes of aluminium. In response to these challenges, a novel controlled process based on injector casting is being developed. →

This paper presents a conceptual approach and a successful implementation guide for a digital twin of a modular manufacturing system. →

The production industry has the potential to make a significant contribution to the transition towards a more sustainable future. Given the pressing nature and growing popularity of this topic, a considerable number of producing companies have already implemented sustainability goals. Nevertheless, at the operational level, decisions are still largely driven by economic considerations. →