Theses

Key Words:

Disaster Management, Preventive Citizen Participation, Information Management, Mobile App Development, Storm Surges & Flooding, Prototyping, Artificial Intelligence, and Machine Learning

Background:

Within the research project Rescue-Mate, the challenge of creating situational awareness and optimizing information flows in the security scenario of a storm surge in Hamburg is being investigated. This challenge arises against the backdrop of rising sea levels, more frequent extreme weather events, and a growing number of residents who may need to be evacuated and provided for in the event of a disaster. The key stakeholders in this scenario include government agencies and their affiliated units such as the fire department, police, the harbor authority, and various relief organizations. Comprehensive situational awareness and an informed and prepared population are particularly relevant for disaster response planning in situations like a storm surge, which involves aspects such as drowning individuals, drifting ships, and blocked roads.

It is possible to write theses that contribute to the content and objectives of the Rescue-Mate project, exploring selected subject areas and developing and testing prototypes. Through the project consortium, access to experts can also be provided.

Research Objectives:

Depending on the academic level (bachelor's or master's), specific areas of focus, and interests, various tasks and research questions can be addressed. Collaborations with organizations and/or companies are also possible but not mandatory. The following thematic areas are of particular interest and can be explored:

• Analysis and development of concepts and mobile prototypes for informing and proactively educating citizens in urban areas, particularly in Hamburg-Mitte, as well as their involvement in crisis situations to efficiently collect data and support crisis-relevant stakeholders.
• Development of AI-based prototypes for the automated evaluation of data from mobile crisis applications, such as aggregating automatically collected data, including locations, and presenting them to crisis management teams in the form of interactive maps or similar tools.

These topics are especially suitable for students majoring in computer science, business informatics, human-computer interaction, software system development, IT management and consulting, and intelligent adaptive systems. Theses can be written in either German or English.

Key Words:

Smart City, citizen participation, urban planning, co-creation, design, machine learning, natural language processing, data science, 3D Unity.

Background:

Urbanization has led to strong growth in cities and metropolitan regions in recent years, and various organizations predict that this development will continue in the coming years. For example, the UN has calculated that by 2050, over 70% of citizens worldwide will live in urban environments, which corresponds to an increase of up to 25%. However, growth rates vary greatly by country and region. In Europe, for example, almost 60% of citizens already live in cities, which means that local growth here will only average around two percent, while in Africa and Asia, it could reach 4% to 5%. This development creates new conflicts and promotes existing conflicts that affect urban mobility, the housing market, and recreational areas, for example, and affect each other in different ways.

For the (further) development of cities, responsible stakeholders are increasingly relying on the involvement of citizens and local political organizations and associations to enable participatory design in construction projects, for example. In addition, digital discussions in social media and in the political arena (including citizens' and district assemblies) generate further data and information that is aggregated in resolutions. For planned or desired construction measures, resolutions are of great interest to both the implementing actors and the government, as the resulting orders can lead to (political) action and, thus, to urban changes. Resolutions and topics of the district assemblies are of particular importance here, as they have a direct influence on developments in the neighborhoods and districts. Accordingly, it is helpful if these are recognized at an early stage so that representatives and citizens are involved in development and coordination processes at an early stage in order to enable co-creative design and give participants the opportunity to participate in the development of their own environment.

Research Objectives:

Depending on the degree course (Bachelor's or Master's), content focus, and interest, a wide variety of tasks and research questions can be worked on. Cooperation with organizations and/or companies is also possible but not required. The following topics are of particular interest and can be worked on:

• Analyze and develop prototypes to support digital citizen participation in urban or rural areas with the aim of offering these in a scalable and efficient manner. The quality of contributions is also crucial here, as digital scenarios differ from on-site formats, and different circumstances, functions, and designs appeal to different target groups. The focus here is on web applications, mobile applications, and AI-supported interactions using classic ML models as well as large language models.
• Development of prototypes for the automated evaluation of data from citizen participation. The increasing success of digital participation highlights the interest of citizens in helping to shape their environment and, as past projects have shown, leads to better results and urban planning projects. At the same time, the volume of data collected in citizen participation projects is increasing, which makes evaluation more difficult and increases costs. It is therefore necessary to investigate how evaluation processes can be automated and experts supported with the help of AI/ML. Theses on this topic can be written in cooperation with the State Office for Geoinformation and Surveying, and existing data from past participation projects using the Hamburg platform DIPAS can be published as required and in coordination with the cooperation partners.
• Systematic comparison of existing machine learning methods for natural language using full texts and/or tweets to summarize and/or evaluate texts with regard to content and/or sentiment (sentiment analysis). Studies based on real participation data and/or data from social media are of particular interest. Aspects such as human-computer interaction or human-AI interaction and trust in AI evaluations should also be investigated, which is particularly possible on and through the development of (web) prototypes.

The topics can be worked on in cooperation with various partners of the working group, such as the State Office for Geoinformation and Surveying as well as private providers of participation systems, associations and others.

The topics are particularly suitable for students of computer science, business informatics, human-computer interaction, software system development, IT management and consulting, and intelligent adaptive systems.
These can be written in German or English.

Key Words:

Smart Mobility, Design, Decision Support Systems, Machine Learning and Data Science.

Background:

Urbanization has led to strong growth in cities and metropolitan regions in recent years, and various organizations predict that this development will continue in the coming years. For example, the UN has predicted that by 2050, over 70% of citizens worldwide will live in urban environments, which corresponds to an increase of up to 25%. An effective and efficient public transport network is needed to support and ensure coexistence and mobility in the long term.

In Europe, the (further) development of the mobility sector is strongly influenced by the European Green Deal, which prescribes the transformation towards sustainable transport. This means, for example, that all public transport companies must switch to emission-neutral drive technologies by 20230. Many companies are focusing on electric drives, although alternative technologies based on hydrogen, for example, are also possible. In addition, many cities are increasingly developing into intelligent and networked environments that provide a wide range of data and information about the mobility behavior of citizens, vehicles, and cyclists. This and other data, e.g., about the weather, can be used to develop new mobility services and intelligent systems that support existing services.

Research Objectives:

Depending on the degree course (Bachelor's or Master's), content focus, and interest, a wide variety of tasks and research questions can be worked on. In addition, cooperation with organizations and/or companies is generally possible but not required. The following topics are of particular interest and can be worked on:

• Analysis and development of prototypes to support prediction systems for e-buses, metro and comparable domains in order to optimize the use of vehicles and strengthen offers. The focus here is on the use of AI, machine learning and data science to predict various values.
• Simulations of mobility behavior and presentation in situation reports or interactive prototypes to support mobility services and decisions by responsible companies as comprehensibly and transparently as possible. Aspects from the fields of AI, machine learning and data science as well as the areas of human-computer and human-AI interaction must be taken into account.
• Use of generative AI systems in customer service, e.g., to answer questions or process complaints through prototype implementations and evaluations with the target groups.
• Development of IT and AI systems for user groups with special requirements in terms of usability and, in particular, accessibility. In principle, this can affect all areas and aspects of mobility and follows the goal of providing all people with easy and fast access to public transport.
• Conceptual work on the mobility of tomorrow and how technologies such as autonomous driving and concepts such as "car-free city centers" can promote sustainability while offering a high level of mobility.

The topics can be worked on in cooperation with various partners of the working group such as Hamburger Hochbahn AG or Hamburger Verkehrsverbund GmbH, which is strongly recommended for selected topics.

The topics are particularly suitable for students of computer science, business informatics, human-computer interaction, software system development, IT management and consulting, and intelligent adaptive systems.
These can be written in German or English.

University-Industry Collaboration (UIC) provides a platform for knowledge exchange, collaborative research, and the practical application of AI in partnership with companies. Through this collaboration, students can benefit from expert knowledge and hands-on experiences to develop their competencies in data analysis, machine learning, neural networks, and other AI technologies.

The impact of this collaboration on students' employability is multifaceted. On the one hand, practical training in cooperation with companies enables students to acquire relevant knowledge and skills increasingly demanded by employers. This can enhance their employability and give them a competitive advantage in the job market.

Furthermore, University-Industry Collaboration can also promote students' entrepreneurial skills and entrepreneurship. Through direct interaction with companies and involvement in AI solution development projects, students are encouraged to develop and implement innovative ideas. This contributes to fostering entrepreneurial thinking and action and may inspire them to start their own businesses or drive innovative AI applications within existing companies.

Task assignment:

Depending on the chosen degree program (bachelor or master), academic focus, and individual interests, there are various opportunities for assignments and research questions. There is also the option to collaborate with external organizations and/or companies, although this is not mandatory. Students have the flexibility to either work with organizations/companies they have acquired themselves or utilize the existing partners of DDLitLab for potential collaborations. The following are some areas of particular interest that can be explored as research topics:

• The role of University-Industry Collaboration in developing AI competencies and its impact on students' employability.
• Analysis of best practices in University-Industry Collaboration in the field of AI to identify the relevant skills and competencies for graduates' employability.
• Examining the impact of University-Industry Collaboration on closing competency gaps in the field of AI and promoting graduates' employability.
• Identifying AI-based tools and technologies that can be utilized in University-Industry Collaboration to enhance skills development and students' employability.
• Researching industry needs and expectations regarding graduates with AI competencies and their influence on curricula and programs in higher education.
• Developing measures and strategies for assessing and validating AI competencies acquired in University-Industry Collaboration to enhance students' employability and facilitate their transition into the job market.
• The impact of University-Industry Collaboration on the development of AI skills and their contribution to sustainable innovation in companies and organizations.
• The effectiveness of University-Industry Collaboration in developing AI competencies and its contribution to the sustainable development of companies and organizations.

Theses can be written in either German or English.