Available

From RPA Code to BPMN Process Diagrams

This thesis investigates methods for automatically transforming robotic process automation (RPA) code into BPMN process diagrams to improve process transparency, maintainability, and integration with business process management tools. The work involves analysing RPA scripts, defining mappings to BPMN constructs, and prototyping a converter. The topic comes from Seinäjoki-based IT-consultation company River IT and details of collaboration can be agreed upon.

Prerequisites include solid programming skills, understanding of RPA platforms and BPMN modelling, and ability to read and formalise code-to-model transformations.

Dr. Jussi Rasku

Available

AI-BASS Real Options Subsystem for Startup Decision Modeling

This thesis focuses on developing a microservice-based subsystem within the AI-BASS platform to help startups make smarter investment and resource allocation decisions. The system will identify decision points during AI-BASS chat sessions, model them as real options, and visualize their dependencies to support data-driven decision-making.

The student should understand strategic management and AI in business, be able to conduct systematic or grounded literature analysis using qualitative or text analysis tools such as NVivo, Atlas.ti, or Python, and be capable of synthesizing findings into a conceptual framework. Strong academic writing skills and independent analytical thinking are essential.

Dr. Jussi Rasku

Mikko Auranen

Available

AI-Supported Strategy in Software Companies: A Qualitative Mapping of Practices and Implications

This thesis explores how artificial intelligence is used in strategic planning and decision-making within software companies. The student will review academic and industry sources, analyze patterns in how AI supports strategic processes, and develop a conceptual framework linking AI-assisted strategy to decision-making quality and outcomes.

The student should be fluent in Python, LLM APIs, and comfortable with microservice architecture and MongoDB. They must understand and willing to learn of basic decision analysis and real options theory, and be able to translate these into computational models. Experience with data integration, network models, visualization frameworks, and applied AI systems is essential. The work requires technical autonomy, analytical rigor, and the ability to validate and document a novel subsystem through prototype development.

Dr. Jussi Rasku

Mikko Auranen

Available

Adaptive Language Models for Game Rule Generation and Quality Evaluation 

This thesis will explore how language models can be adapted to generate and evaluate novel game rules in the context of both card and abstract board games. Inspired by Browne and Maire's work on evolutionary game design, the research aims to create a simulation environment where language models generate game rules and assess the "quality" of these games based on parameters like drama, uncertainty, and playability. The models will be benchmarked against a suite of metrics to optimize for engaging gameplay, informed by principles from recent studies in game design and AGI experimentation (e.g., ARC-AGI, AlphaCode). 

Prerequisites: Proficiency in LLM use and APIs, experience with Python, and an interest towards evolutionary algorithms. Familiarity with game design concepts. 

Dr. Jussi Rasku

Available

Smart and Sustainable Energy Consumption Optimization with IoT and LLMs

Are you interested in developing AI-powered solutions for energy efficiency? This Master's thesis project focuses on creating an IoT-based energy management system where Large Language Models (LLMs) analyze and optimize energy consumption. The project could involve integrating energy meters and IoT sensors, such as temperature, humidity, and electricity usage monitors. It could also include developing AI-driven analytics to provide smart recommendations for optimizing energy consumption. Additionally, a user-friendly interface could be created for energy management and forecasting. By reducing energy consumption and improving decision-making, this project contributes to sustainability and the circular economy. If you are passionate about AI, IoT, and green technology, this is your chance to make an impact.

Prerequisites: Python, LLM. IoT -devices Possibility to use/implement APIs: https://github.com/Green-Software-Foundation

Dr. Mika Saari

Available

Gameful LLM-Based Multi-Agent Systems

This thesis aims to assess what gamefull elements can effectively influence a Multi-Agent system's performance in coding tasks. By introducing gamefull elements in a Multi-Agent system, we hope to encourage collaboration (or competition), leading to more refined output.

Prerequisites: Familiarity with how ML/LLM models work (training, evaluation, datasets, etc.) Willingness to learn or experience with interviews, thematic analysis, or survey design

José Siqueira de Cerqueira

Available

Can I Trust This Model? A Study on How Documentation Shapes Trust in Open-Source AI

This thesis explores how open-source AI model documentation — especially model cards — influences developers’ trust in the models themselves. As developers rely on documentation to assess risks and reliability, the study investigates which elements (e.g., training data, use cases, benchmarks, limitations, licensing) most impact perceived trust. Through semi-structured interviews, it will assess how developers interpret documentation. The study also examines whether transparency and explainability increase model trust, and whether developers distinguish between trusting the documentation versus the model. Findings will inform better documentation practices to support trustworthy AI development. Prerequisites: Familiarity with how ML/LLM models work (training, evaluation, datasets, etc.) Willingness to learn or experience with interviews, thematic analysis, or survey design.

José Siqueira de Cerqueira

Available

Impact of generated PlantUML diagrams on large language model generated code accuracy

The performance of an LLM generating or fixing code depends on the correct context in which the edits are made. Hence, the research question is: Can the performance of the coder AI be improved by enriching the prompt with generated UML diagrams? The diagrams will be generated from relevant project source files and given to the LLM using a suitable DSL such as PlantUML. The feasibility of the approach is tested on an SWE- bench, some subset of that, or some other coding benchmark. 

Prerequisites: The student has experience ising coding LLMs such as Github Copilot, Qwen 2.5, Mistral ,or DeepSeek Coder. Also, one should be fluent in creating and reading UML, know how to write PlantUML, and be interested in empirical testing of software tools. 

Dr. Jussi Rasku

Available

Ethical issues in AI agents and multi-agent collaboration: a systematic literature review

As AI continues to progress, hypothetical future risks become the reality of today. AI agents, both lone agents and AI agents working collaboratively, present various promises across a wide variety of industries, but also various risks. Discussion on these systems has accelerated with recent advances in Generative AI (GenAI) and Large Language Models (LLMs).  

A study systematically reviewing this research, so as to gain an overview of ethical risks and mitigation measures already acknowledged in literature, presents a timely contribution to the field of AI ethics. 

Dr. Kai-Kristian Kemell

Available

Automated SOFTWARE QUALITY ASSURANCE using LLM

Use of Large Language Models to automate various aspects of Software Quality Assurance, such as code review, bug detection, and test case generation. The goal is to enhance the efficiency and accuracy of the SQA process by integrating LLMs to analyze codebases, detect vulnerabilities, and improve overall software quality.  

The project will involve experimenting with state-of-the-art LLMs like GPT to fine-tune their capabilities in understanding and validating code.   

The research will also focus on developing practical methods for integrating LLMs into existing software development pipelines, making the process more seamless and accessible for software engineers. The student will work with industry-standard tools and frameworks, collaborating closely with experts in software engineering and AI/ML to achieve innovative solutions for automating quality assurance tasks. 

Prerequisites: Strong programming Skill, understanding of Quality Assurance and proficiency in software testing, knowledge of LLMs and familiarity with AI/ML 

Shahbaz Siddeeq

Available

Comparative Study of Finnish capable Language Models over several NLP Tasks

The thesis will focus on evaluating language models fluent in Finnish, like Poro and Viking, across various tasks such as text generation, translation, and question answering. They are compared against the state-of-the-art commercial offering such as ChatGPT models. The student will benchmark the models in both CPU and GPU environments and assess their performance and accuracy. 

Prerequisites: Familiarity with large language models, natural language processing and machine learning concepts. Experience with benchmarking tools and Hugging Face models is recommended.

Dr. Jussi Rasku

Available

A Lightweight Command Line GPU Time Allocation System for a Multi-Organization Shared Server

This project aims to design and implement a lightweight, single-server, command-line-based GPU allocation system used by multiple organizations. The system will enable users to reserve GPU time efficiently, show the reservations, and prevent the use of unallocated resources, ensuring fair usage across various projects. The student will also compare the solution against existing systems in terms of performance and ease of use. 

Prerequisites: Strong programming skills (preferably C), knowledge of Linux systems and command line interfaces, and experience with distributed computing environments. 

Dr. Jussi Rasku

Available

Sustainable Software Engineering (with AI) 

Examples of possible topics: "Reducing the Carbon Footprint of AI Model Training: A Study on Pruning, Quantization, and Transfer Learning", "Life Cycle Assessment of AI Models: From Training to Deployment", "AI-Specific Hardware for Green Computing: A Comparative Study of GPUs, TPUs, and AI Chips"

Prerequisites: https://github.com/Green-Software-Foundation

Dr. Mika Saari

Available

Automating Cloud Infrastructure Management with Language Models: Generating Infrastructure-as-Code via Terraform from Natural-Language query

This thesis aims to proposes an automated framework that leverages Large Language Models (LLMs) to translate natural-language cloud infrastructure requirements into executable Terraform scripts. By integrating LLMs into Infrastructure-as-Code (IaC) workflows, the approach enables intelligent, efficient, and error-resistant cloud service management across multiple providers. It highlights how AI-driven IaC generation can streamline deployment, reduce human intervention, and improve scalability in DevOps environments.

Prerequisites: Basic knowledge of cloud computing (AWS, Azure, GCP or CSC).

Hands-on experience with Terraform or other Infrastructure-as-Code tools. Proficiency in Python for automation and using LLM APIs.

Understanding of DevOps workflows (CI/CD, version control).

Familiarity with prompt engineering and basic AI/ML concepts.

(Optional) Experience with Docker/Kubernetes and cloud SDKs (e.g., boto3, Azure SDK).

Md Mahade Hasan

Available

Improving Long-Horizon Planning in LLM-Based Agents: Hierarchical Reasoning, Search-Augmented Inference, and World-Model Integration

Objective: Investigate limitations of current LLM agents in long-horizon tasks. Design a hierarchical decomposition framework with verifier guidance. Integrate tree-search (MCTS/LATS-style) into inference-time reasoning. Explore world-model-based planning for simulating future states. Develop mechanisms for backtracking and replanning under uncertainty. Evaluate performance improvements on benchmark environments.

Ayman Asad Khan