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Faculty of Engineering

Faculty of Management and Economic Sciences

Faculty of Design

Master Thesis Research Fields and Topics

Winter 2023/24

Part I: Engineering (MET/IET/MCTR)

Machine Learning for Image and Signal Processing

Prof. Dr. Turker Ince turker.ince@giu-berlin.de

Machine Learning-Based Motion Artifact Correction from ECG Signals

Biomedical signals such as ECG recordings often suffer from a blend of artifacts including noise, motion artifacts, and attenuation of amplitudes. Historically, there have been significant efforts to denoising ECG signals. However, the presence of motion artifacts may also degrade biomedical signals significantly and adversely affect analysis and interpretation of them. This thesis study aims to investigate the performance of different machine-learning methods for correcting and reducing motion artifacts from ECG recordings (especially those acquired from wearable ECG sensors). Real ‘clean’ Holter recordings such as in the reference MIT-BIH ECG database will be used to emulate the effect of motion artifacts and analyze and compare the performance of different ML approaches both quantitatively and qualitatively for reduction of them.

Figure. A sample real Holter ECG recording corrupted by motion artifact, noise and attenuation, and the corresponding output of an unsupervised ML model.

Advanced Machine Learning-Based Analysis of Spaceborne SAR Imagery for Improved Prediction and Assessment of Impact of Fires

Early detection of active fires and accurate prediction and assessment of their impact are crucial. Recent advances in machine learning and remote sensing technologies allow more accurate localization and mapping of burned areas and improved prediction of damage in buildings. This thesis study proposes new compact machine learning models based on self-organized operational neural networks to efficiently process high spatio-temporal resolution SAR imagery in near-real time. Comparisons of the performance and computational complexity of the proposed method to the traditional statistical-based and more recent deep learning techniques will be made for processing and analysis of the recently acquired real-world datasets.

Figure. Sample input SAR image patch from a real fire event (left), and trained ML model output classifying building damage from fire (right).

Performance Comparison of Unsupervised vs. Supervised Machine Learning Methods for Blind Underwater Image Restoration

Underwater images can be distorted by a combination of several sources such as color distortion, light scattering, flickering, uneven illumination, and poor visibility due to transmission characteristics of light and water medium. In addition to the traditional image processing and statistical methods, recently, an increasing number of deep learning models with supervised and unsupervised learning have been proposed as a solution to the challenging problem of underwater image enhancement and restoration. In this thesis study, the performance and computational complexity of different ML approaches for this problem using well-known benchmark datasets are

Figure. Sample underwater image and its restored version using an unsupervised Operational ML model.

A Comparative Performance Analysis of Different Techniques for Blind Restoration of Real-World Audio

Real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. Despite numerous studies proposed for audio restora­tion in the literature, most of them focus on an isolated restoration problem such as denoising or dereverbera­tion, ignoring other artifacts. For this reason, their restoration performance for real-world audio signals is usually limited. This thesis study investigates the performance of well-known signal processing and statistical-based methods as well as recent deep learning approaches for the restoration of corrupted real-world audio signals.

Figure. A sample set of 2-second segments from the TIMIT dataset signals corrupted by different artifacts and the corresponding restored signals.

Using Artificial Intelligence and Multi-agent Systems / Securing Mobile Sensors

Prof. Dr. Mohamed Khalgui   mohamed.khalgui@giu-berlin.de

Securing reconfigurable mobile sensors in WSNs

We deal in this master thesis with reconfigurable wireless sensor networks under harvesting energy constraints. These networks are deployed to collect data from their environment, analyze and send them to final users. A reconfiguration is any run-time scenario that adapts the software as well as the hardware architecture of the network according to user requirements. Since wireless communication is energy consuming, these networks are composed of fixed and mobile sensors that move autonomously and change their positions in the considered plan in order to improve the quality of service required by users and also to meet the energy availability in their batteries. We aim in this master thesis to secure the behavior and the mobility of these sensors in order to protect the global network at run-time. In fact, any suspected trajectory of mobile sensors should be well detected. This master thesis will be applied to a real-case study in order to highlight the planned theoretical contributions.


Using AI for improving automobile traffic in complex smart cities

This master's thesis focuses on the advanced management of road traffic in complex smart cities with an objective to limit traffic jams and to reduce effects of automobile accidents. We want to use artificial intelligence to analyze traffic in real time and to act dynamically on the duration of red lights in different directions of traffic and at different daily times. Example: we aim to act on traffic lights at 7 a.m. so that large flows of cars are served as a priority towards workplaces. In this case, the traffic lights will be smart and reactive. A multi-agent architecture with a real-time coordination protocol will be proposed to synchronize the various red lights in operation and limit delays linked to traffic jams or in the occurrence of road accidents, which disrupt traffic. This master's thesis will be applied to a real case study in order to highlight the planned theoretical contributions.


Using AI for improving performance of smart irrigation systems

Today, global warming and water availability constitute the most serious problems facing our shared planet. One of the solutions applied in agriculture is the deployment of drip irrigation systems to save the quantities of water to be exploited. This system relies on distribution valves and electronic controllers applying well-defined irrigation strategies set by experts. Our goal is to use AI to deploy intelligent and reliable irrigation agents that consider the quantity of available water and predict future water needs to ensure a quality agricultural product while managing sudden breakdowns that may occur in irrigation circuits. The thesis will propose a multi-agent architecture as well as a coordination protocol to synchronize the different distributed agents. This master's thesis will be applied to a real case study in order to highlight the planned theoretical contributions. Keywords: Smart irrigation system, AI, Multi-agent, Fault recovery, Data prediction, IoT.


Machine Learning and Distributed Systems

Prof. Dr. Mohamed Ehsan Ashour   mohamed.ashour@giu-berlin.de

Machine Learning based analysis of sports movement  

Accurate measurement and characterization of movement is a key factor to evaluating and enhancing the performance of competitive athletes. In racing sports such as running and swimming evaluating the details of theses movement requires extremely experienced coaches. The objective of this master research is use a combination of camera recording and acetometer sensors worn by the athletes to estimate the power and speed of the movement. Machine learning combined with knowledge of the sport bio-dynamics is used to obtain accurate estimations and propose movement modifications.  

Machine learning based distributed allocation of cognitive radio resources

Introduction: Cognitive radio (CR) is technology that aims to improve radio spectrum utilization by allowing users to access the radio resources opportunistically. The efficient allocation of these spectrum resources in efficient coordinated and distributed manner is a challenging problem. This research focuses on how to use machine learning to improve the distributed allocation of spectrum resources in CR networks The thesis will use a simulation of realistic CR network model to evaluate the performance of different machine learning-based spectrum allocation algorithms.

Cloud Radio Access Network Assisted Massive random access:

Massive random access (MRA) is a key technology for enabling massive connectivity in future wireless networks. However, the large number of users and the high signaling overhead of MRA pose significant challenges to the radio access network (RAN). This thesis focuses on evaluating MRA in CRANs the objective is to identify the key factors affecting the performance and to dimension the number of users that make use of such a network. The thesis will use a combination of analytical and simulation models to study the impact of various factors, such as the number of users, the backhaul capacity, and the cloud processing power, on the performance of CRAN-assisted MRA. It will also investigate the use of efficient distributed approaches to tune the network performance.

Intelligent Scheduling Mechanism in Edge Network for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a rapidly growing field that connects physical devices, sensors, and actuators to the internet. This thesis focuses on the challenge of scheduling tasks in edge networks for IIoT. It will have to decide whether these tasks can be executed on the device, the edge or on the cloud. The scheduling objective is increase response while minimizing the device energy consumption. The thesis will use a combination intelligent techniques, optimization and simulation to evaluate the performance of different scheduling mechanisms.

Optimized task partition and allocation on distributed multiprocessors systems

Distributed multiprocessor systems (DMPs) offer the potential to improve performance and scalability. However, the efficient allocation of tasks to processors in DMPs is a challenging problem. This thesis will focus on the different factors to consider when partitioning and allocating tasks in DMPs. The thesis will use a combination of analytical and experimental methods. The analytical methods will be used to develop new task partitioning and allocation algorithms.


3D Printing and Smart Materials

Prof. Dr. Mahmoud Kadkhodaei   mahmoud.kadkhodaei@giu-berlin.de

An investigation into buckling of 3D-printed PA12 auxetic structures

Auxetic structures have a negative Poisson’s ratio and have applications in metamaterial systems. When incorporated into ordinary structures, they promote the buckling load due to their unusual deformations. Buckling of selective auxetics, designed and produced by 3D printing of Polyamide-12 (PA12), is investigated in this thesis. Different structures by varying the geometric and dimensional parameters are produced, and the influences of these parameters on buckling of the products are experimentally studied.

Investigation of 3D-printed PA12 under cyclic loadings

Polyamide-12 (PA12) is a widely used material in 3D printing of polymeric parts for various applications. Recent investigations have shown that a combination of hyperelasticity, viscoelasticity, and plasticity appears in the mechanical behaviors of 3D-printed PA12 parts. In this thesis, standard test samples are produced, and empirical force-displacement responses under different types of cyclic loadings are studied. A material model is further adopted to describe the experimental finding.


Electromechanical responses of shape memory alloy spring actuators

Shape memory alloys (SMAs) are smart materials with the capability of remembering their original configuration by recovering large inelastic deformations upon heating. SMA springs can generate both tensile and compressive forces under electric actuation, and these details are investigated in the present thesis. Combinations of bias (ordinary) and SMA springs are considered, and their mechanical response are studied for different electric heating schemes.

Artificial Intelligence and Internet of Things

Prof. Dr. Amr Talaat Abdel-Hamid   amr.talaat@giu-berlin.de   

Pedestrian Behaviour Analysis using AIOT

This research explores how AIoT technologies can analyze and understand pedestrian behavior, offering insights into crowd dynamics and urban planning. By leveraging AI and IoT, it seeks to enhance safety and efficiency in urban environments. 


Compressed Sensing for Retrospective Computing using In-Memory Computing Techniques

This research investigates the application of compressed sensing and in-memory computing techniques for retrospective data analysis. It aims to efficiently process large datasets by reducing memory requirements, potentially revolutionizing data analytics and storage solutions. 


Power Analysis for Physical Layer Security of LoRa Node

Focusing on LoRa-based IoT devices, this study delves into the power consumption aspects of physical layer security. It aims to detect rouge nodes through power analysis and optimize power usage while ensuring robust security measures for LoRa nodes in low-power, long-range communication systems. 


Road Surface Deterioration Detection using AIOT

Utilizing AIoT, this project aims to detect road surface deterioration, which is critical for infrastructure maintenance. It combines AI for image analysis and IoT for data collection to create a system that can efficiently monitor and assess road conditions. 


Trust Management for Advanced Metering Infrastructure

Trust management in Advanced Metering Infrastructure (AMI) focuses on safeguarding data integrity within IoT networks. It addresses threats like fraud and tampering by exploring efficient solutions such as physically unclonable functions (PUF). These approaches aim to ensure data integrity while meeting the low bandwidth and power requirements of AMI systems. 


Controlled Environment and Mobile Robots

Dr. Pablo Zometa    pablo.zometa@giu-berlin.de


Controlled Environment in Agriculture

One of the major challenges in optimizing resource utilization and maximizing production yield in food production lies in the complexity of the controlled system. Highly complex mathematical nonlinear dynamical models of the environment and biomass growth exist. Such models describe the relation between outside weather (uncontrolled environment), and inside environment (controlled) variables like temperature, humidity, CO2 concentration, etc. However, it is not trivial to accurately estimate the environmental parameters nor to estimate biomass growth in praxis. The following master’s thesis topics attempt to tackle these challenges.

  • Estimation of Biomass Growth using Moving-Horizon Estimation

Using a mathematical model of the environment and past sensor measurements, moving horizon estimation (MHE) attempts to estimate the current biomass growth, considering known constraints, e.g. the relative humidity RH typically satisfy 0 ≤ RH ≤ 100%. The performance of the MHE is to be compared to existing methods used to estimate biomass growth.

  • Estimation of Biomass Growth using Unscented Kalman Filter

Using a mathematical model of the environment and current sensor measurements, and unscented Kalman filter (UKF) attempts to estimate the current biomass growth. The UKF is well suited for system in which the estimation relies on information from various sensor (sensor fusion) and the system dynamics are nonlinear. The performance of the UKF estimation is to be compared to existing methods used to estimate biomass growth.

  • Modeling of Environment using Operational Neural Networks

Operational neural networks (ONN) are a generalized nonlinear extensions to the commonly used convolutional NNs. ONN-based models have been successfully applied to solve challenging signal and image processing tasks. In particular, ONN-based ML models have been shown to improve performance with respect to state-of-the-art CNN-based methods by achieving higher learning capacity and at the same time near real-time working capability with reduced computational costs. Using synthetic data (i.e., generated using an existing mathematical model) describing the environment, the objective of this thesis is to learn a compact representation of the environment dynamics using operational neural networks (ONN).

Mobile Robots

Low-cost mobile robots commonly rely on microcontroller units (MCU), which have limited computational capabilities, e.g., small RAM, and ROM capacity. In many cases, advanced control and state estimation algorithms are needed to improve the robot’s performance. The following topics explore how to estimate the robot position using advanced control methods on embedded devices.

  • Neural Network Approximation of Moving Horizon Estimation

Moving Horizon Estimation is an advanced algorithm based on repeated optimization. That is, at each sampling period, using a set of past measurements y and inputs u and a dynamical model x’ = f(y, u), we estimate the current state x by solving an optimization problem. In particular, the measurements y come from the robot’s sensors (encoders, accelerometers, camera, etc.), the control inputs u are the torque applied to the motors, and the state x is the robot’s position in a map.

This topic explores two different ways to approximate the MHE. Instead of repeatedly solving an optimization problem at each sampling period (which is computationally demanding), we use neural networks to learn the static map x = M(y, u) offline. We used the trained network online to estimate the state x at each sampling period for any given y, u.

There are different NN architectures: here we explore feedforward neural networks, and more compact representations using product quantization and operational NN. The final stage of this topic is the deployment of the trained neural network on an MCU (Hardware) to evaluate the actual computational demands of the resulting approximation.

  • Estimation of Robot Position using Visual Odometry

Using visual information from a single camera, we want to estimate the position of a robot in a map. We rely on ready-to-use neural network models to extract the required odometry information from camera images. In this practical work, we will use a Coral Dev Board Micro to evaluate the performance of the odometry algorithm. The Coral Dev Board Micro is an embedded board that integrates a low-resolution color camera, a Tensor Processor Unit (TPU), and a dual-core Cortex-M microcontroller. Initially, the algorithm may be developed using general-purpose hardware, like a usb camera and a laptop (simulation).

The final stage of this topic is the deployment of the algorithm on the embedded board (hardware) to evaluate its actual performance. The performance criteria include accuracy, energy efficiency, execution speed, and memory requirements.

Part II: Management



Prof. Dr. Melike Demirbag-Kaplan    melike.demirbag@giu-berlin.de

Sustainable Marketing  

This research area delves into the multifaceted aspects of sustainable marketing, seeking to identify innovative strategies that promote environmentally friendly practices and socially responsible products and services.  

Consumer Perception and Adoption of Sustainable Products: A Cross-Cultural Analysis 

This research will explore how consumers from different cultures perceive and adopt sustainable products, considering cultural influences on attitudes and behaviors towards eco-friendly offerings. Cross-cultural analysis can be conducted for Egypt vs. Germany.  

Sustainable Marketing Metrics and Performance Measurement 

This research will focus on developing a framework for evaluating the effectiveness of sustainable marketing initiatives, incorporating both quantitative and qualitative metrics to assess environmental and societal impacts, as well as financial outcomes. 

Circular Economy Practices in Sustainable Marketing: A Case Study of (Selected Industry) 

Using a specific industry as a case study, this research will assess the integration of circular economy principles into sustainable marketing strategies and its impact on reducing environmental footprints and enhancing sustainability. Thematized industry can be chosen in accordance with the interest of the student.  

Digitalization in Marketing  

In this area, the focus is on harnessing the potential of emerging digital technologies, such as artificial intelligence, big data, and marketing analytics, in revolutionizing marketing strategies and enhancing customer experiences. 

Personalization and Privacy: Balancing Customer Experience and Data Security in Digital Marketing 

This research will investigate how companies can strike a balance between delivering highly personalized customer experiences through digital technologies while ensuring the protection of consumer data and privacy rights. Intended research approach would be a qualitative inquiry.  

AI-Powered Chatbots in Customer Engagement: Evaluating Effectiveness and User Satisfaction 

This thesis topic will analyze the impact of AI-powered chatbots in enhancing customer engagement and satisfaction, considering factors like conversational design and automation efficiency. An experimental design is proposed.  

The Role of Data Ethics in Digital Marketing: A Comparative Study 

The aim of this research is to compare the approaches and practices of companies in different industries or country specific regulations regarding data ethics in digital marketing, focusing on data collection, usage, and transparency, and its effects on consumer trust. 

Transformative Consumer Research  

TCR aims to create meaningful and transformative change by examining the role of consumers and businesses in addressing social, environmental, and ethical concerns, and to contribute to a more sustainable and inclusive society while driving positive impact for consumers and businesses alike. 

Cross-Cultural Perspectives on Consumer Activism: A Comparative Analysis  

This topic aims to compare and to contrast consumer activism movements in two different countries, examining cultural influences, government regulations, and societal factors that shape the nature and effectiveness of consumer advocacy in addressing social and environmental concerns.  

Consumer Well-being in the Age of Digital Consumption 

This research will explore the relationship between digital consumption habits (e.g., social media, online gaming) and consumer well-being, assessing the role of digital platforms in promoting or hindering mental and physical health. 

Promoting Inclusivity in Marketing: Examining Representation and Diversity 

This thesis aims to analyze the portrayal of diverse communities in marketing campaigns and media, evaluating the impact of inclusive representation on consumer perceptions, and exploring strategies for more inclusive marketing practices. A cross-cultural comparison may also be intended as the research approach. 


Prof. Dr. Martin Walther    martin.walther@giu-berlin.de

Corporate Disclosures and ESG Scores

The EDGAR database offers annual and quarterly disclosures of many companies that are freely available. It is an open discussion whether these reports include valuable information or are boilerplate. This thesis aims to contribute to this discussion by examining whether the percentage of text dedicated to each of the constituents (environmental, social, governance) helps explain (changes of) ESG scores. 

Motives and Timing of Strategic Investments

Strategic investments are supposed to be long-term and also pursue non-financial goals. The Eikon database contains the shares of strategic investors of a large selection of U.S. companies. The research idea of this thesis is to examine (changes in) these shares, in order to identify drivers of strategic investments. Furthermore, the timing of these investments should also be investigated.

Strategic Investors and Corporate Stability

Related to Topic 2a, the research objective of this thesis is to investigate whether the presence of strategic investors improves the stability of companies. Potential variables of interest include ratings and the performance (risk-adjusted returns) during times of crisis, such as the financial crisis in 2008/09 and the COVID-19-crisis. 

Technical Analysis and Co-movement of Stock Returns

Although questionable from an academic point of view, technical analysis, such as chart analysis, is a popular tool and likely to be used by a large share of retail investors. Therefore, technical indicators could lead to similar trading patterns that ultimately lead to an increase in the co-movement of stock returns. The objective of this thesis is to derive technical indicators for a relevant set of stocks, and evaluate their ability to predict the future correlation of stock returns. 

Part III: Architecture

Prof. Dr. David Calas    david.calas@giu-berlin.de

Housing – Living Today and Tomorrow

How can our housing environment be inclusive, liveable, sustainable, and affordable in the face of current changes as well as crises, and what can architecture contribute to housing today and tomorrow? The focus of the topic navigates around theories, concepts, potential policies, strategies, and solution attempts.

  • At least three (3) of the below keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic #1.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic (#1) and selected keywords.

Keywords: Affordability, New forms of housing, Mix of Uses, Refurbishment/Reuse of Existing Buildings, District Development, Process Development, Theory of housing, Climate Adaptation and Ecological Sustainability, Inclusive Planning


Socio Urbanism – The City that Leaves Nobody behind

We are living in an urbanized world. Today 55% of the world population live in urban areas. How do cities contribute to the protection of its inhabitants in a structural, economical, and sanitary way? And how do cities adapt to the noticeable effects of climate change? How can living together be designed and organized in a sustainable way? Which role plays public space among increasing inequality? Which policies can be put in place to ensure healthy, secure, and prospering living circumstances for all?

  • At least two (2) of the below keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic #2.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic (#2) and selected keywords.

Keywords: Smart City, Participation, Public Space, Doughnut Economics, Urban Theory, Urban Policies, Equality, Urban Economics


Urban Food Production – Eating the City

1/3 of our planet consists of ice-free land surface. 40% of it is suitable for agricultural purposes. 70% of increased food supply is expected until 2050. 80% of the globally produced food is consumed in urban areas. These numbers give a quite clear picture which role the city of the future will play. They also imply a significant integration of food systems in the urban environment as a logical consequence.

  • At least two (2) of the below keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic #3.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic (#3) and selected keywords.

Keywords: Urban Food Production, Renaturation, Food Production Systems, Edible City, Urban Gardening, Responsible Food Consumption, Biodiversity, Edible Architecture



Prof. Dipl.-Ing. Dunja Karcher    dunja.karcher@giu-berlin.de

Learning from Vernacular Architecture - Cradle to Cradle

In the area of learning from vernacular architecture, research explores constructive design and climate-smart architectural and building practices. By examining traditional building practices and indigenous architectural wisdom, the aim is to uncover innovative approaches that promote sustainability and contextual appropriateness. Why is vernacular architecture relevant today?

  • At least three keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic 4.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic and selected keywords.

Keywords: Vernacular Design, User Participation, Cultural Heritage and Sociology, Local Technology and Materials, Traditional City, Sustainable Architecture and Urbanism, Climatic Design/passive cooling, natural ventilation, thermal comfort, Cradle to Cradle, Biophilic Design.

The Spontaneous - mixed-use cooperative

The research within “Rethinking Typologies and Construction Methods” focuses on the valuable insights that informal settlements offer. By examining their self-regulating systems, sustainable building practices and design techniques. What can, building groups and housing cooperatives worldwide learn from informal settlements?

  • At least three keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic 5.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic and selected keywords.

Keywords: Informal Settlements, Cooperative Housing Models, Social-ecological Transformation, Affordable Housing, Living-Working Housing Modell, Development of Housing-Typologies, Educational Buildings and Schools, Flexible Typologies, Resilient Urban Strategies.


Lifelong Learning and Artificial Intelligence

The question arises as to what architecture teaching will look like in the future. Exploring the potential of artificial intelligence as a tool; the role of AI in shaping the future of architecture; Implementation and impact on architectural education. AI will change the way architects work in the near future. How can sustainable strategies for studying and teaching architectural education be developed? Which general design guidelines have to be considered when using AI as a design tool?

  • At least three keywords should be applicable on the student’s Master Thesis topic.
  • The individually formulated research question must be referenced to the general Master Thesis topic 5.
  • The student’s autonomous approach to the general topic narrows down to a specific given field/area of interest considering topic and selected keywords.

Keywords: Future of Architecture, Modern Construction Methods, AI in Construction, AI in Infrastructure, AI in Urban Planning, AI in Pre-Design, AI for Building Information Modeling BIM, AI in architectural education.

Part IV: Design

Graphic Design

Prof. Dr. Felix Kosok    felix.kosok@giu-berlin.de

Typography & Identity

Language is the first medium through which we understand our world. But language isn't just the spoken word, but a structure of differences in letters, words, and ultimately meaning. The forms and shapes of those letters matter. How do we interact in intercultural settings? How do typefaces influence the meaning of words? How does typography change with an associated culture, if new words and meanings are constructed? The research questions aim at a new understanding of the role of typography, through which habitual visual hierarchies can be challenged and new, hybrid formats of cultural exchange and interaction can emerge.

Possible subtopics: Graffiti, Calligraphy, Letters of Berlin, Typography & Fashion, Berlin’s Flyerdesign, An Archive of Letterforms, … 


Communication Design & Culture

Graphic design is the interface through which we connect with society, through which we form our own identities and in turn communicate visually to the outside world. How do visual languages of hybrid cultures work? How can design be used as a tool for empowerment, self-realization, and resilience? Projects within this research focus could analyze communication in public space by official bodies as well as by civic agents. What is the visual materiality of our everyday culture and what can we learn from analyzing it? Projects could furthermore explore the visual repertoire of different subcultures. What are the forms and symbols used to construct different and new identities? What visual codes exist within this different cultures?

Possible subtopics: Cultural Visual History of a City (e.g. Posters of Berlin), An Analysis of (Sub-)culture Communications (e.g. Stickers, Flyers, Poster), Sign Systems in Public Spaces, Popular Media, Inclusive Design in Public Spaces …


Co-Designing the Future

Graphic design as we know it today is a product of modernity, even though graphics existed long before the industrial revolution. From modernity, even contemporary graphic design has inherited the concept of the genius designer, the star designer, who designs everything by him/herself. Facing multiple crises, we need a new concept of design. How can we re-conceptualize graphic design to be a collective and communal practice? What forms of graphic design already exist that are practiced together with others, maybe even human and non-human actors? This research focus wants to question the singular designer and explore new concepts of interconnectedness and collaboration.

Possible subtopics: Design Collectives, Working with Natural Materials in Design, Co-Designing with Nature, Participatory Design, Public Interest Design, Design as a Process


Product Design

Prof. Meike Langer    meike.langer@giu-berlin.de

Food Culture

Getting together at the table with family and friends and sharing food, consolidates our social bonds and reflects our cultural heritage. For generations festive seasons are celebrated with special dishes and by cooking and eating together. Today our eating culture is undergoing rapid changes.

In the Master Thesis, the role and cultural heritage of one’s food culture should be reflected and changes analysed. The results should be embodied in a highly innovative design. The design can range from industrial products, to small serial production, to full product collections and products with a spatial relation.


Closing the Loop

Many key factors of sustainable products are determined by the design. Hence the influence of designers on sustainable developments is extremely high.

In the Master Thesis, the focus lies on circular design. The key factors of circular product design are reflected and analysed. Existing businesses, their designs, production, material choices and supply chains are researched and reflected. The results are embodied in an innovative concept and design for a circular product or product collection, preferably in connection with a start-up idea.



Home office, flying desk, meeting cubes or relaxation areas - how and where we work today is wide ranging. The many “tools” we depend on such as furniture, products and technical devices need to adapt to our requirements.

In the Master Thesis, the focus lies on today’s working culture. Students should reflect and research on the specific cultural aspects of it. They should define a field of interest within the current developments and identify the stakeholders (human-centered design). The results serve as foundation for creating scenarios and subsequently innovative design solutions.