Skip to content
Main menu hidden.

Degree projects – Department of Computing Science

Below you find a list of degree projects in collaborations with our research groups or external partners.

Collaborate with us

Are you searching for students to your research group, project, company or organization? Please contact Jan-Erik Moström, Director of Studies at the Department of Computing Science.

Degree projects – our research groups

Are you a fast learner and like to work with Robotics/AI (30 ECTS)

Intelligent Robotics Research Group develops formal and non-formal methods for intelligent robot behavior, such as visual perception or natural language usage. We are interested in algorithm development as well as interaction and design studies. Are you a fast learner, like to work in a team, have excellent communication skills, and would like to work in Robotics/AI? 

More information
Please contact Thomas Hällström, Professor at the Department of Computing Science via e-mail for a discussion about a suitable thesis project (30 ECTS).

Dou you want to detect emerging cyberattacks or protect user's private data? (30 ECTS)

Autonomous Distributed Systems Lab - This wings of Lab investigate security and privacy, distributed machine learning, trustworthy machine learning issues to build intelligent, adaptive, trustworthy, and resilient systems that operate in complex and real-world environments. To help in our research, we are currently looking for Masters students who want to do their thesis project (30 ECTS) in our group. More specifically, we have several openings in:

  • How to detect emerging cyberattacks (e.g., distributed denial of service) in complex systems?
  • How to maximize the discovery capability of (deep) machine learning algorithms while maintaining data privacy with a minimum amount of resources?
  • How to ensure data uncertainty together with privacy in federated learning?
  • How to optimize performance for machine learning clusters via scheduling and sprinting policies?
  • How to learn from small data?
  • How to protect users' private data for learning at the edge?

For more information
Does this seem interesting to you? Please contact Monowar Bhuyan, Assistant Professor via e-mail: for a discussion about a suitable Master thesis project.

Privacy Issues with Machine Learning in Medical Imaging (30 ECTS)

Machine learning has become very important in medical sciences as a means to automate for instance diagnosis and prognosis, and to personalise medicine and treatments. Machine learning is for instance used in medical imaging to automate parts of the radiation treatment planning after a cancer diagnosis.

We are curious about what exactly is learned about particular patients when building deep convolutional neural networks for medical imaging applications. Since models trained on patient data are commonly and freely shared online, there are potential privacy issues involved that are currently not fully understood. We are therefore looking for Master's thesis students who are interested in projects in this area.

For more information
If this sounds like something for you, please contact Tommy Löfstedt via e-mail: for more information, on this and other potential projects.

Interested in working on Responsible AI design and human-centered AI?

The Responsible Artficial Intelligence Group are notably interested in studying the ethical and societal impact of AI and developing tools and methodologies to mitigate adverse effects; as well as agents that can better connect to humans and societies. We are open to a variety of proposals, from ethical assessments, to using our design methodologies and tools for concrete applications; from hard software for demonstrations to scientific activities.

As a specific example of a pursued track, we developed in house a cognitive component for replicating anxiety and its avoidance; and we wish to make a cool demo of it.

What if you would expand a video game with an anxious character? Or make a cool VR game for which the player can parametrize the stress level he/she will likely face instead of the "difficulty rating" of the game? What about helping us pushing the borders of science, and develop the next version of our anxiety-sensitive AI system? 

For more information
If these are activities you may like, please contact Loïs Vanhée, Associate Professor at The Department of Computing Science via e-mail:

AI-Enhanced Knowledge Harvesting via Heterogeneous Data Analytics & Federation

Deep Data Mining Group has been working on multimodal heterogeneous data analytics and data federation by applying the techniques of text mining, information retrieval, natural language processing, machine learning, and differential privacy. The main research topic are data driven and application-oriented such as entity-based social network analysis (e.g., sentiment, emotions, political view etc.), personal privacy analysis, and ontology-based knowledge graph construction etc. Additionally, the group has several connections to industry and academia who are interested to co-supervise master thesis projects, where we can conduct some interesting AI related interdisciplinary research (e.g., Covid pandemic event tracking and inference, medical screening, energy consumption and anomaly prediction, genetics&healthcare etc.). We welcome an open discussion regarding master thesis plan.

For more information
If you are interested, feel free to contact Lili Jiang, associate professor at the Department of Computing Science via e-mail: about a suitable master thesis project.

Cloud/edge + AI + hyper-distributed applications (30hp)

Autonomous Distributed Systems Lab: The group's main focus is new cloud/edge technologies with enhanced performance enabled by AI for the data processing required to support future hyper-distributed applications. Based on such the cloud/edge, we research data markets and data economy; therefore, we are currently looking for Master's students who want to do their thesis project (30hp) in our group. More specifically, we have several openings in:

a) Data markets allowing for data assets to be discoverable, efficiently and fairly priced, and shared/traded in a compliant way.

b) Scalable and AI-powered data management for data usability in different contexts, covering data provenance, data quality management, improving data interoperability, and metadata management.

For more information
Please contact Antonio Seo,, to discuss/define a thesis project.

Improved understanding of the importance of training for deep neural networks

When training deep neural networks in machine learning, the standard optimisation method is some variant of stochastic gradient descent. An hypothesis about why deep neural networks can generalise as well as they do, despite them typically having many more parameters than training data, is the utilisation of stochastic gradient descent. The hypothesis says that when using stochastic gradient descent, wide/flat minima are preferred over sharp/narrow ones, that stochastic gradient descent has the ability to "shake itself out" of sharp minima.

The goal of this project is to investigate different ways that stochastic gradient descent can work as a form of regulariser for the model, how it can avoid overfitting the training data. There are several different ways this can be tested, and the project would amount to select one or a few approaches, implement, and test them by comparing theoretical and practical results.

For more information
If this sounds like something for you, please contact Tommy Löfstedt, associate professor at the Department of Computing Science, through e-mail: for more information on this, and other potential projects.


Degree projects – external partners

Develop new features for international Evity (15 or 30 ECTS)

Evity is a startup in Umeå that develops an HR tool for small and medium-sized companies. We use automation and form tools to create KPIs and processes in the tool. Now, we are looking for a student who wants to work in .net within Azure where we can find a collaboration to develop new features or improve existing ones. Size of project 15 or 30 ECTS.

We are an international team as our co-founder and CTO is from New Zealand, where he also works from. You can read more about us on

Please contact CEO, Tommy Eriksson, telephone: 070-696 70 22 or email:


Interested in learning about the state-of-the-art deep learning models in medical image analysis? (30 ECTS)

NONPI Medical develops and implements software tools for medical image analysis. The proposed project focuses on deep learning methods for medical imaging, e.g. tumor segmentation, super-resolution, or distortion correction.

There are many different frameworks available for implementing deep learning models, which is good for development and research, but also divides the field and published code into fractions, depending on the particular framework that was used. The result is that the field is divided, and the results are difficult to reproduce between framework users. An open source standard for machine learning models has been established (ONNX), however this format is yet to be commonly deployed, and especially so in medical imaging research and development.

To help spread developed models, and the ONNX format, we are currently looking for:

(a) master's thesis student(s) (30 p) who want to establish an open source collection of current state-of-the-art deep learning models and methods for medical imaging, mainly challenge winners, state-of-the-art models, and models from well-cited research papers. The project involves collecting and categorising pre-trained models, converting them from a diverse set of formats to the ONNX format, and testing them on medical image data in MICE Toolkit, a software for medical imaging developed by NONPI Medical AB.

For more information

If you are interested in learning about the state-of-the-art deep learning models and unifying the research community, please contact 


Deep learning models for medical image processing

NONPI Medical AB develops and implements software tools for medical image analysis. The proposed project focuses on developing tools to design, alter, and train deep learning models and full machine learning pipelines for medical image processing, such as tumour segmentation, super resolution, or distortion correction.

There is much interest within for instance radiation therapy to use deep learning models for automation, but many in such fields lack the background to make substantial changes in existing models, and to develop their own specific models programmatically. With this background, NONPI Medical AB has developed a graphical tool to define the architecture for deep learning models. The existing tool is built on top of Python, which adds an extra layer of complexity since the rest of the program is written in C#, and also has problems whenever the underlying deep learning framework is updated.

The goal of this project it to evaluate different approaches to build deep learning models modularly in C#, and to find efficient graph algorithms to execute different steps of the machine learning pipeline on available hardware resources.

The goal of these developments is to aid the adaptation of existing deep learning models, and to make it possible for more researchers and medical practitioners to develop their own and be able to change or improve existing models and the entire maching learning pipeline. NONPI Medical AB is therefore looking for (a) master's thesis student(s) (30 hp) that wants to develop such tools for use within medical image analysis.

For more information
If you are interested in learning more about the project or how we use state-of-the-art deep learning models, please contact: