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== Joint PhD Program with University of Sydney ==
From September 2024 on there will be the possibility to start a joint PhD with the University of Sydney (Australia). PhD students will stay in both Göttingen and Sydney for at least one year and can achieve two PhD degrees.
For more information, please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de].


In November/December 2023, Fabian visited research groups in Melbourne and Sydney. Impressions of his visit can be seen here: [[Media:australia.pdf | pdf]]
== Joint Thesis Topics with fortiss ==


The following Master's thesis topics are offered in collaboration with Prof. Rute Sofia at fortiss, the Research Institute of the Free State of Bavaria for Software-Intensive Systems, in Munich.


=== * '''New!''' Cognitive Network Intelligence for Energy-Efficient and Deterministic Industrial Communication ===


== Open Theses and Student Project Topics ==
Industrial applications increasingly rely on heterogeneous networks combining 5G, Wi-Fi, and Time-Sensitive Networking (TSN). This topic investigates energy-aware traffic engineering to reduce network energy consumption while meeting strict latency, jitter, and reliability requirements. Tasks include developing energy models, designing optimization mechanisms, and evaluating them using the ns-3 simulation framework. We expect you to have programming skills in Python or C/C++ and an interest in network optimization. A background in wireless communications, industrial networking, or network simulation is an advantage.


The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].
[https://www.fortiss.org/en/career/masters-thesis-cognitive-network-intelligence-for-energy-efficient-and-deterministic-industrial-communication Further information]


* (B) Bachelor thesis
=== * '''New!''' Energy-Efficient IoT-Edge-Cloud Infrastructures for Dynamic AI Services ===
* (M) Master thesis
* (P) Student project


Deploying AI inference across heterogeneous edge and cloud resources requires balancing inference quality, latency, and energy consumption. This topic focuses on energy-aware scheduling of small large language models (LLMs), building on the energy monitoring infrastructure in the fortiss Industrial IoT Lab. Tasks include characterizing inference energy consumption and developing scheduling strategies across different hardware configurations and model quantization levels. We expect you to have a background in machine learning, distributed systems, or computer networking, together with programming skills. Familiarity with PyTorch or Hugging Face is an advantage.


===  * '''New!''' Efficient Live Volumetric Video Streaming System===
[https://www.fortiss.org/en/career/masters-thesis-energy-efficient-iot-edge-cloud-continuum-infrastructures-for-dynamic-ai-service-support Further information]


The exponential growth of digital data and multimedia content necessitates robust and efficient systems to handle the streaming of high-resolution, three-dimensional volumetric videos. These videos offer a more immersive and realistic experience, making them increasingly used in various sectors such as virtual reality, augmented reality, and entertainment. The challenge here lies in creating a system that can handle the high-bandwidth and computation-intensive demands of live volumetric video streaming while ensuring the delivery of a seamless and high-quality user experience. This project conceptualizes the development and optimization of efficient algorithms and systems to handle volumetric video streams, mitigating bandwidth cost and latency issues. We expect you to have a background in video streaming technologies, computer vision, and programming skills.
=== * '''New!''' Decentralised Federated Learning for Semantic Communications ===


Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]
Semantic communication aims to reduce communication overhead by transmitting information relevant to a specific inference or control task. This topic investigates decentralised federated learning for training and adapting semantic encoder-decoder models across heterogeneous industrial IoT, edge, and cloud environments. Tasks include developing a distributed learning architecture, addressing non-IID data and device heterogeneity, and evaluating task performance and communication overhead through simulation. We expect you to have programming skills and a background in machine learning or distributed systems. Familiarity with federated learning frameworks or ns-3 is an advantage.


===  * '''New!''' Edge-Cloud Orchestration for LiDAR-based Traffic Analysis===
[https://www.fortiss.org/en/career/masters-thesis-decentralised-federated-learning-for-semantic-communications-in-iiot-edge-cloud-environments Further information]


The imminent era of smart cities and autonomous vehicles paves the way for the deployment and operation of advanced monitoring and processing systems. Among these, LiDAR technology stands out for its ability to provide high-resolution, three-dimensional traffic data, becoming an essential component for efficient traffic analysis and management. However, the computation-intensive and latency-sensitive nature of LiDAR data processing poses significant challenges and dictates the need for efficient orchestration between edge and cloud computing resources. Edge-Cloud Orchestration offers an innovative solution to this problem by bridging the gap between these two technologies, enabling the low-latency processing of complex LiDAR data. It would be good if you have a background in point cloud processing/cloud computing, K8s, and programming skills.
=== * '''New!''' Semantic-Aware Routing for Multi-Hop Industrial IoT Networks ===


Please contact Yanlong Huang[yanlong.huang@cs.uni-goettingen.de]
Routing decisions in industrial IoT networks can affect how well transmitted information supports an application’s task. This topic investigates routing mechanisms that account for task requirements and semantic distortion across multiple network hops. Tasks include designing a protocol that uses distortion budgets and contextual knowledge to select forwarding paths, implementing it in ns-3, and evaluating it against conventional routing approaches. We expect you to have a background in computer networking or wireless communications and programming skills in Python and C/C++. Familiarity with network simulation is an advantage.


===  * '''New!''' Blockchain-based Spectrum and Computation Resources Sharing in Mobile Networks===
[https://www.fortiss.org/en/career/masters-thesis-semantic-aware-routing-for-multi-hop-iiot-infrastructures Further information]


The sixth-generation (6G) system is widely envisioned as a global network consisting of pervasive devices that interact with each other. Besides exchanging information, these peer entities also share heterogeneous and distributed network resources. Blockchain is a promising technology to secure resource sharing in a peer-to-peer way. We need students for this topic. We expect you have a background in computer network and programming skills in Python.
For more information about joint supervision, please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de].


Please contact Jin Xie [jin.xie@stud.uni-goettingen.de]
== Joint PhD Program with University of Sydney ==
From September 2024 on there will be the possibility to start a joint PhD with the University of Sydney (Australia). PhD students will stay in both Göttingen and Sydney for at least one year and can achieve two PhD degrees.
For more information, please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de].


===  * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===
In November/December 2023, Fabian visited research groups in Melbourne and Sydney. Impressions of his visit can be seen here: [[Media:australia.pdf | pdf]]


Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure.  We need students for this topic. We expect you have a background in knowledge graph and programming skills in Python.


Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]


===  * '''New!''' Using LLM for Knowledge Graph Completion (B/M/P)===
== Open Theses and Student Project Topics ==


Large language models (LLMs), such as ChatGPT and GPT-4 (OpenAI, 2023), have extensive internal knowledge repositories from their vast pretraining corpora, which can be used as an extra knowledge base to alleviate information scarcity for the long-tail entities in Knowledge Graphs. However, there is no effective workflow design for LLM on KGC tasks. How to leverage the LLM to perform reasoning on the KG Completion (KGC) task is a noteworthy and significant topic. We need students for this topic. We expect you to have a background in knowledge graph and LLMs, you'd better have a programming skill in Python.
The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].


Please contact Tong Shen [shen.tong@cs.uni-goettingen.de]
* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project


===  * '''New!''' Tree Growth Detection using Satellite Images and Computer Vision Methods (B/M/P) ===


A tree planting project in Madagascar was initiated several years ago. The outcomes of this project shall now be evaluated by analyzing satellite images of the study area with Computer Vision methods. In a first step, very high resolution (VHR) satellite images from 2023 with a resolution of 0.5m will be used to identify trees with object detection / semantic segmentation. In the next step a lower resolution (5m) satellite image time series starting in 2015 will be used for change detection to identify, in which locations the project was  (un)successful.


Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]
===  * '''New!''' Using LLM for Sign Language Translation (B/M/P)===


===  * '''New!''' Emotional Support Conversation Generation based on LLM (B/M/P)===
Sign language is the primary means of communication for the deaf and hard-of-hearing community, yet most people do not understand it. This topic explores the integration of Large Language Models (LLMs) with computer vision to build an advanced sign language translation system - with special focus on overcoming the critical challenge of understanding long, continuous sign language videos. We welcome students passionate about Natural Language Processing (NLP) and Computer Vision (CV) to explore the cutting edge of sign language translation technology.


Emotional support conversation aims to reduce individuals' emotional distress through social interaction and help them understand and cope with the challenges they face. Using LLM to provide emotional support is a promising technology which can be used in customer service chats, mental health support and so on. We need students for this topic. We expect you have a background in dialogue generation and programming skills in Python.
Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]


Please contact Jing Li [jing.li@cs.uni-goettingen.de]
===  * '''New!''' Using LLM for Sentiment Knowledge Graph Construction (B/M/P)===


===  * '''New!''' Rumor control and detection method for social networks based on GCN===
Constructing a sentiment knowledge graph using Large Language Models (LLMs) like ChatGPT involves leveraging the model's capabilities to understand and analyze textual data, extract entities and relationships, perform sentiment analysis, and organize the information into a graph structure. We expect you have a background in knowledge graph and programming skills in Python.


In social networks, rumors spread quickly and have a wide impact. Through GCN, the complex relationships between nodes in the network can be effectively captured, and the detection and propagation paths of rumors can be modeled and controlled. The core of this method is to improve the ability to identify and control the spread of rumors by jointly learning user behavior, information content, and network topology by building an information propagation graph. We need students in this topic. We expect you have a background in rumor detection and programming skills in Python.
Please contact Wenfang Wu [wenfang.wu@cs.uni-goettingen.de]
 
Please contact Fei Gao [fei.gao@cs.uni-goettingen.de]
 
===  [Occupied]  Image-to-Image Translation of Different Nightlight Image Types (B/M/P) ===
 
Nightlight intensities have been proven to be a good indicator for socio-economic status. However, for long-term temporal analyses their use can be challenging, as different satellites for sensing nightlight intensities operated at different times (DMSP OLS 1992-2014 and VIIRS 2012-2023). Both types differ not only in resolution, but there is also a big discrepancy in the optical appearance and value ranges. To obtain consistent nightlight images for temporal analysis, Image-to-Image Translation methods shall be used in this project/thesis for the conversion between both types. Finally the performance of the translated and original nightlight images for a regression on socio-economic indicators shall be evaluated.
 
Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]


===  [Occupied] Satellite Image Indices and Machine Learning for Socio-economic Estimation (B/M/P) ===
===  * '''New!''' Cooperative Perception and Resource Optimization in Vehicular Networks (B/M/P)===


There are several indices, which can be derived from satellite images. For example the Normalized Difference Vegetation Index (NDVI) indicates the presence and condition of vegetation, while the Normalized Difference Built-up Index (NDBI) indicates the presence of built-up areas such as buildings or roads. The distributions of these and other indices may have different explanatory power to estimate the socio-economic status of locations. Therefore in this project/thesis the regression performance of machine learning models - using statistics of these indices as features - to estimate socio-economic indicators shall be evaluated for the individual and also combined indices. Optionally, Convolutional Neural Networks (CNNs) can be applied additionally, which take the derived index images as input.  
Cooperative perception enables vehicles and roadside infrastructure to share sensing information and better understand their surroundings. This topic explores joint sensing, communication, and computing resource optimization to improve perception performance while reducing latency and resource consumption. We welcome students with a background in wireless networks, edge computing, optimization algorithms, and Python programming.


Please contact Fabian Wölk [fabian.woelk@cs.uni-goettingen.de]
Please contact [Yuxin Zhang] [yuxin.zhang@stud.uni-goettingen.de].


===  * 3D natural hazard simulator  ===
===  * 3D natural hazard simulator  ===
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Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)
Please contact Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de]] (B/M/P)


===  * Privacy-preserved Video Analytics===
===  * '''[Closed]''' Privacy-preserved Video Analytics===
This project/thesis topic focuses on the protection of privacy in video analytics.
This project/thesis topic focuses on the protection of privacy in video analytics.


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[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].
[1] Pecam: privacy-enhanced video streaming and analytics via securely-reversible transformation [https://dl.acm.org/doi/abs/10.1145/3447993.3448618].


===  * AI for networking adaption  ===
===  * '''[Closed]''' AI for networking adaption  ===
In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).  
In this project/theses topic, you will explore how to make AI meets networking requirements (e.g., fluctuating network states).  
You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.
You will (1) deploy and test Genet[1]; (2)extend the Genet environment to multi-client environment (e.g., ABR); (3) deploy multi-agent algorithms on Genet and valid the performance.
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