Theses

The Project Thesis topics are now available. Please apply by Monday, 26 October, via studienarbeiten.w12(at)tuhh.de.

In your application email, please include your preferred topic, name, matriculation number, degree program, and the names of the students you would like to work with. Groups will consist of 2 to 4 students.

Before applying, please check the regulations of your degree program to ensure that you are eligible to complete a Project Thesis at our institute.

After the application deadline, you will be informed about the topic allocation by email. The working period will be 6 months from the specified starting date.

Please send your application for a specific topic from the list below directly to the researcher responsible for supervising that topic.

Your application documents should include:

• An up-to-date curriculum vitae
• Your most relevant certificates and qualifications

If no open topics are currently offered at the Institute of Maritime Logistics or if they do not fit your interests, you have the opportunity to request further topics. Please keep in mind that our capacities are unfortunately limited and therefore not every inquiry can be answered positively. For this reason, you should try to find a topic sufficiently in advance.

In order to reduce the workload for all participants and at the same time increase your chances of success in finding a suitable topic, please follow the procedure described below:

1. Inform yourself about the research activities of the Institute of Maritime Logistics.
2. Please select one of the main research areas mentioned by the staff of the Institute of Maritime Logistics. This can be either in terms of content or method.
3. Send one e-mail to one of the researchers, that cover your desired research topic, with the following information:
    
   • Course of studies, semester
   • Selected research focus. If this is a content focus, please also indicate your preferred method, e.g. simulation, benchmarking, forecasting.
   • Curriculum vitae
   • Current transcript of records
4. Please wait for feedback by mail. We will do our best to answer you as soon as possible. Due to the necessary internal coordination processes, this may still take a while.

Background of the Thesis

Port operations involve high risk activities in which human error can lead to accidents, injuries and operational delays. Although Unity based training simulations are widely used, most systems only evaluate mistakes after they have occurred. This thesis focuses on integrating artificial intelligence into port training simulations to predict unsafe actions, provide real time warnings and link user errors to potential real-world port accident scenarios.

Objective of the Thesis

The objective of this thesis is to develop an AI based adaptive warning and reporting system for port training simulations, with the aim of improving safety and efficiency in the maritime sector. This system will enhance an existing Unity based training environment by monitoring trainee behaviour, predicting potential errors, providing real time guidance and generating accident related safety reports. This study also aims to provide real time warnings to trainees during the Mixed Reality (MR) visualization of the Unity based training model.

Task

The thesis will involve reviewing literature on port accidents and identifying common human errors in port operations. An existing Unity based training module and its Microsoft HoloLens 2 visualization will be enhanced with AI based functionality. User behaviour data will be analyzed to predict unsafe actions and provide real-time adaptive warnings. The system will also generate reports linking user errors to potential port accident scenarios, and assess the effectiveness of AI supported training.

Prerequisites

Unity, C#, MR/VR teknolojileri ve programlama konusunda temel bilgiye sahip olunması tavsiye edilir. Yapay Zeka, Makine Öğrenimi veya simülasyon tabanlı eğitim sistemlerine aşina olmak faydalı olacaktır. Denizcilik operasyonları, güvenlik sistemleri ve kullanıcı davranış analizi konularına ilgi duymak da tezin başarıyla tamamlanması açısından avantaj sağlar.

Processing is possible as of now!

Please send your application with your CV and current grades to:

Background
Container terminals are key transshipment nodes in the maritime supply chain, where containers move from one mode of transport to another. Maritime transport involves at least two container terminals: at one, the container arrives from the surrounding area or hinterland and is loaded onto a vessel; at the other, it arrives by vessel, is unloaded and is subsequently transported to the surrounding area or hinterland by train, inland waterway vessel or truck.

Many shipping lines operate a hub-and-spoke network, in which these two terminals are often connected via one or more hubs rather than directly. This is broadly comparable to public transport: in many cases, a container needs to change vessels between its port of origin and its port of destination. Container routing is the task of selecting a sequence of terminals and vessels for each container that ensures it reaches its destination on time.

Objective
You will investigate the current state of operational container routing in industry and academic research, identifying differences between the two. You will examine academic research through a literature review, clearly distinguishing between existing approaches. You will assess current industry practice through publications, blog posts and recent job advertisements from shipping lines.

Throughout your investigation, you will focus on the approaches shipping lines use to route containers through their own transport networks and how they respond to delays and disruptions. A further consideration is whether these approaches plan transport on a door-to-door or pier-to-pier basis. Does the shipping line’s transport network comprise only seaports, or does it also include logistics nodes in the hinterland, such as intermodal terminals?

Requirements
A systematic and rigorous approach to academic research, along with basic knowledge of mathematical optimisation and maritime logistics, is expected.

Please send your application, including your curriculum vitae and current transcript of records, to:

Background

Maritime logistics is under considerable pressure to drastically reduce its greenhouse gas (GHG) emissions while container throughput continues to grow. A key measure for decarbonising container terminals is the electrification of horizontal transport. While conventional diesel-powered terminal tractors can be refuelled flexibly, operating battery-electric vehicles requires precise coordination between transport operations and charging events to maintain terminal performance.

Objective

The aim of this thesis is to develop a mathematical description of the energy consumption and charging processes of electric terminal tractors. First, their specific driving and operating profiles (e.g. empty trips, loaded trips and waiting times at cranes) will be systematically analysed. Based on this analysis, models will be developed to represent the vehicles’ energy state over time and enable a mathematical comparison of different charging strategies (e.g. opportunity charging versus depot charging).

Tasks

1. Process analysis: Systematically investigate terminal tractor operations at container terminals, taking electrification into account.
2. Model development: Formulate mathematical models of energy consumption and charging cycles (state-of-charge profiles) based on current academic literature.
3. Scenario comparison: Compare different charging concepts in terms of charging times, duration and power.
4. Visualisation: Present the results graphically to illustrate sensitivities (e.g. the impact of battery capacity or charging power on vehicle availability).

Requirements

Good academic performance, a rigorous approach to academic research and basic knowledge of maritime logistics are expected.

Please send your application, including your curriculum vitae and current transcript of records, to: 

Background of the Study

Container terminals are key transshipment hubs in the maritime supply chain, where containers change modes of transport. Today, these transshipment processes are mostly coordinated by Terminal Operating Systems (TOS). These are complex IT solutions that ensure short vessel turnaround times through the intelligent use of heavy equipment. In practice, rolling time slots are used for the internal assignment of work orders to heavy equipment within the terminal. This aspect is only sparsely addressed in the scientific literature.

Objective of the Thesis

Using a structured literature review, you will assess the current state of research. You will select a specific subproblem at a container terminal, such as the use of storage cranes or horizontal transport equipment. For this area, you will identify which publications use rolling time windows for planning work orders. You will classify the publications based on the circumstances under which work orders are rescheduled or reassigned to heavy equipment, and document the duration of the time windows and the method used for order assignment (e.g., mathematical optimization or heuristics).

Task Description

First, conduct a free-text search to identify the initial source publications. From these, derive your initial keywords. It is also important here to identify synonyms and related concepts. Select a method for structured literature review and apply it. Document your progress thoroughly, for example, using reference management software.

Prerequisites

In addition to strong academic performance, a sound scientific approach and fundamental knowledge in the field of maritime logistics are expected.

Work on this project can begin immediately!

Please send your application, including your resume and current transcript, to:

Background of the Thesis

Seaports serve as a link between international and regional transport systems. When planning their construction or introducing major operational changes, simulation is often used as means to estimate their effects. One key aspect here is the expected yard occupancy. However, obtaining realistic operational data for simulations is often difficult due to confidentiality. To overcome this, ConFlowGen (https://github.com/1kastner/conflowgen) was developed, enabling the creation of synthetic yet realistic container flow scenarios using vessel schedules and further assumptions, such as the modal split and the expected container dwell time.

Objective of the Thesis

At the institute, we have used ConFlowGen to estimate the required yard capacity before (see, e.g., https://link.springer.com/chapter/10.1007/978-3-031-56826-8_13). We have seen that when synthetically generating container booking data, in the first days the yard needs to warm up before the expected yard occupancy is reached. In the last days, operations cool down until the yard is empty again. Only after the warm up and before the cool down, the data approximates actual terminal operations sufficiently well. But how many days does the warm up and cool down take and is there some automated way to identify it? You analyze this for selected ports over a period of several weeks or months using publicly available sources.

Task

You start by a sound literature research related to warm up and cool down phases in discreteevent simulation. Then you select exemplary ports/terminals which provide open data. Then you explore different approaches for identifying the normal operations window (e.g., fixed thresholds, statistical detection methods). The most promising approaches you implement in Python as an extension for ConFlowGen.

Prerequisites

A systematic and well-founded scientific approach, good knowledge of statistics and data science, and basic knowledge in the field of (maritime) logistics are expected. For the data analysis, good programming skills are essential.

Processing is possible as of now!

lease send your application with your CV and current grades to: 

Background of the Thesis

Unmanned surface vehicles (USVs) are increasingly deployed for inspection, monitoring, and autonomous transport in maritime and inland waterways. A key challenge is reliable obstacle avoidance in cluttered environments such as ports and canals. USVs are usually equipped with monocular cameras and can serve as an alternative to traditional distance measuring sensors such as Radar or LiDAR, which may be bulky and expensive. However, estimating distances from a monocular camera remains challenging. Recent advances in computer vision enable monocular distance estimation, enhancing real-time obstacle avoidance for USVs.

Objective of the Thesis

The aim of this thesis is to develop and evaluate a monocular vision-based system that estimates distances to obstacles in real-time for use in obstacle avoidance in cluttered waterways. The system should support the navigation of USVs in environments such as inland waterways, ports, or canals, where accurate short-range distance estimation is critical to avoid collisions with floating objects, maritime vessels, or infrastructure.

Task

The work involves a comprehensive literature review of existing methods for monocular distance estimation, with a focus on applications of monocular distance estimation in maritime and inland waterways. Based on this, a suitable deep learning-based system needs to be implemented. The system will be trained and tested on publicly available datasets. The final model should be capable of detecting and estimating obstacle distances in real time from monocular video input. The system will be validated through quantitative metrics.

Prerequisites

A systematic and well-founded scientific approach, good knowledge of statistics and data science, and basic knowledge in the field of (maritime) logistics are expected. For the data analysis, good programming skills are essential.

Processing is possible as of now!

lease send your application with your CV and current grades to:

Background of the Thesis

To reduce emissions and traffic congestion at container terminals, so-called Truck Appointment Systems (TAS) have become widely adopted; under these systems, truck drivers must reserve a time slot in advance at the relevant container terminal for delivery. TAS settings must be adjusted based on the specific characteristics of each container terminal and port. Simulation-based optimization can support this process.

Objective of the Thesis

The objective of this thesis is to develop a simulation-based optimization method for the structured selection of suitable parameter settings for TAS at container terminals and to apply it to an existing simulation model. This is intended to enable targeted parameter variation and the corresponding selection of relevant TAS parameters (e.g., time slot lengths, capacity quotas, flexibility rules) in order to increase efficiency, reduce congestion, and lower emissions.

Tasks

Familiarization with the functionality of TAS and analysis of the key control variables.

Examination of the existing simulation model with regard to its structure, input variables, and relevant output variables.

Selection and justification of a suitable optimization method (e.g., heuristic methods, multi-criteria optimization) that can be integrated with the simulation.

Development of a simulation-based optimization method that systematically tests, evaluates, and improves different parameter values.

Conducting experiments with the developed method based on the existing simulation model to identify efficient parameter settings.

Analysis and evaluation of the results with regard to the target variables.

Discussion of the limitations of the developed approach and derivation of recommendations for practical application and further research

Requirements

In addition to strong academic performance, a sound scientific approach and fundamental knowledge in the field of maritime logistics are expected.

The project is available immediately!

Please send your application, including your resume and current transcript, to:

About us

Fraunhofer Center for Maritime Logistics and Services CML is one among the 80 research institutes of the Fraunhofer-Gesellschaft. The Fraunhofer CML develops and optimizes processes and systems associated with maritime supply chain.

Objective of the Thesis

The focus of the thesis aligns with the identification and classification of automated twist lock handling systems. Twist locks are mechanical fixatures used on board vessels to interlock two containers loaded on top of each other. The process of removing and fixing these twistlocks from containers is referred to twistlock handling operations. The process is complicated and requires special attention by port employees to maintain the terminal productivity along with high safety standards. Therefore, the thesis is of great importance.

Task

• To conduct scientific research on the identification of available automated twist lock handling (ATH) solutions present in the market.
• Based on self-understanding, identify and suggest classification criterions for ATH solutions.
• Analyze industry reports and case studies to identify the port of applications for identified ATH solutions.
• Evaluate the impact of automated twist lock handling operations on terminal process.

Prerequisites

Currently pursuing a Bachelor's or Master's degree in Maritime Operations, Port Logistics, Mechanical Engineering, Industrial Engineering, or related fields is required. Along with good academic record fundamental understanding of the technical processes related to ports is preferred. Familiarity with scientific report writing is advantageous. The candidate should demonstrate an independent, results-driven, and methodical approach to work. Additionally, strong commitment, effective communication skills, teamwork capability, and reliability are necessary attributes

Processing is possible as of now!

Please send your application with your CV and current grades to:

Background of the Work

Automatic speech recognition (ASR) systems have great potential for supporting maritime processes, such as the analysis of radio communications or the voice-based documentation of technical procedures. Modern ASR foundation models (e.g., Whisper, Cohere, Voxtral) already achieve high accuracy on general speech data but continue to exhibit significant weaknesses in specialized domains such as the maritime industry. In particular, specialized maritime terminology, limited availability of annotated training data, and challenging acoustic conditions make reliable transcription difficult. Therefore, domain-specific optimization methods are needed that can function even without large amounts of transcribed data (low-resource conditions) in order to flexibly adapt existing models to maritime use cases.

Objective of the Thesis

The objective of this thesis is to develop and investigate approaches for domain-specific optimization of modern ASR models for maritime speech data under low-resource conditions (no, or very little, transcribed training data). To this end, suitable adaptation strategies will be identified, implemented, further developed, and evaluated in terms of their ability to reliably recognize domain-specific speech using maritime use cases as examples. The developed methods will be systematically investigated and compared using appropriate evaluation metrics.

Tasks

The work will initially involve a literature review of existing methods for domain optimization and automatic speech recognition under low-resource conditions. Based on this analysis, one or more suitable approaches for adapting ASR models will be implemented, further developed if necessary, and experimentally investigated. The suitability of the methods will be evaluated using several datasets from the Fraunhofer Center for Maritime Logistics and Services (CML). The evaluation requires the semi-manual transcription of the evaluation dataset (very good German language skills required). Finally, the results will be analyzed and discussed using appropriate evaluation metrics.

Requirements

In addition to a strong academic record, a systematic and well-founded scientific approach and basic knowledge in the field of maritime logistics are expected.

Work can begin immediately!

If you are interested, please send a brief application via email to: