Below is a list of the predefined master's thesis projects available at Simula; the list can be sorted according to the research area of interest.
A Jira extension for Benefits Management in IT-projects
It is important for IT development projects to estimate and keep track of the benefit of the software they are producing. You will help develop tools for this purpose.
A system for planning and analyzing crisis management exercises
Crisis response exercises are held regularly, but often with unclear learning objectives, unplanned data collection and inferior analyses. You will contribute to developing an exercise management system for structured planning, execution and analysis of crisis response exercises.
AI-based virtual avatar for police interview training
We are developing an AI child avatar for the interview training of the police officers to protect vulnerable children from abuse.
Adaptive Tests for Memory Training using Asymmetric Search Point Location
Memory training exercises are known to have a positive effect on improving the memorability of humans. A memory training task can be as simple as trying to remember a password of increasing complexity or a number of varying length.
Automatic detection of abnormal video events in sport videos
Use machine learning and video processing techniques to automatically find events in sports videos. For example, in football, some "easy" ones are goals, but others like tackles are harder.
Autonomous Self-healing Software Systems
Investigate, develop and evaluate data-driven techniques and prototypes that help software engineers build software systems that are autonomously self-healing. These are systems that can understand when they are not operating correctly and, without human intervention, make the necessary adjustments to restore themselves to normal operation.
Benchmarking Modern AI Hardware for Natural Language Processing
A new generation GPUs and AI accelerators such as IPUs promise massive speedups for NLP. Do they work out in practice?
Benchmarking Partitioning tools for Supercomputers
How can we make best use of partitioning software and distribute workloads among compute nodes in a supercomputer?
Benefit points - what will it take for IT professionals to use them
Just as story points are estimates of the cost of a piece of IT functionality, benefit points are estimates of the intended benefit (value for stakeholders) of that piece of functionality. It seems obvious that one should use benefit points in addition to story points, but benefit points (or similar metrics) are not in widespread use.
Building and evaluating a web-based tool for software benefits estimation and management
To help IT professionals optimize the value for stakeholders, you will develop a set of easy-to-use tool for estimating and monitoring how much potential benefit a system under development will produce. You will design the tool based on recent theoretical results, and you will test and evaluate the tool with actual software professionals.
CPU free Programming: When the GPU takes the Lead
New tools can remove inefficiencies in GPU computing, but can we turn that into real performance gains?
Classification confidence visualization of artificial neural networks with adversarial robustness
Adversarial attacks can easily fool artificial neural networks. This project aims to understand these attacks and their defenses by visualizing their classification confidence.
Co-production and co-destruction patterns in systems design, development and use
An IT system is meant for creating benefit for both service providers and service consumers. Often, disbenefit is created instead. You will study the phenomena of co-production of benefit and co-destruction into disbenefit in public service IT systems.
Data Traffic Reduction in Active 5G Measurements
Can we accurately measure the 5G network performance using less data than the standard tools?
Detecting DDoS Attacks in Programmable Data Planes
Building a machine learning model using data plane programming language such as P4 that detect network security attacks at line rate with high accuracy
Development and Testing of Self-Driving Cars
Implementing software tools for designing, developing, and testing of self-driving cars.
Effects of 5G Rollout on End-User Experience
Quantifying the real-life effect of 5G deployment based on Simula's own measurements.
Explainable Artificial Intelligence for data outlier detection
Explainable Artificial Intelligence methods (XAI) represent methods to understand and interpret machine learning (ML) methods, and have recently received a lot of attention. In this project we will also look into if XAI methods can be used to detect data outliers.
Explainable Artificial Intelligence for improved machine learning performance
Explainable Artificial Intelligence methods (XAI) represent methods to understand and interpret machine learning (ML) methods, and have recently received a lot of attention. In this project, we will explore the potential of using XAI to improve the prediction performance of ML methods.
Explainable Reinforcement Learning
In recent years, artificial intelligence has made significant strides in various fields, reshaping the landscape of technology and innovation. One of the key factors driving this progress is the emergence of reinforcement learning (RL) which enables autonomous agents to make decisions and adapt to their surroundings. RL has been very successful in games and other applications. In general, an RL agent aims to learn a near-optimal policy to achieve a fixed objective by taking action and receiving feedback through rewards and observations from the environment. A neural network (NN) commonly represents the policy that, given the observation of the environment state as input, yields values that indicate which action to choose.
Exploring Missing Data Handling and Batch Effects in DNA Methylation Analysis
DNA methylation is a fundamental epigenetic modification within the human genome, influencing various biological processes, including gene expression and cellular development. In the context of methylation data analysis, values typically range from 0 to 100 (or 0 to 1), depending on the chosen scale.
Exploring Multidomain Applications of Large Language Models in Software Engineering
This project meets the demand for enhanced approaches by harnessing LLMs to elevate software engineering practices in specific research domains.
Generative Adversarial Network Models for electoral behaviours studies
Survey experiments in studies of political and electoral behavior using profiles of potential candidates are standard practice in political science. With this type of experiments, the researchers' goal is to test which characteristics voters value most in political candidates. One of the main challenges, however, is to generate realistic profiles to be tested. One of the important components of these profiles are the faces of candidates. A potential option is to use faces of real candidates, but this involves complex legal and ethical issues. Another option is to hire models to represent candidates, but this can be expensive and time-consuming.
Generative machine learning for precision medicine
The accuracy of machine learning models used in clinical decision making has a direct impact on a patient's chances of recovery. Missing data pose a challenge and generative models can assist overcome it.
Generative models with simple temporal structure
Generative models represent a fascinating group of methods that can generate new samples (such as images) with similar properties to the data used to train the generative model. The models have also been used to perform generative forecasts, such as the next frames of a video or the weather for the next hours. However making these methods perform well in such cases is challenging. In this project, we will explore the potential of using simpler models to learn the temporal properties, and only use generative models to learn the spatial dependencies.
Generative properties of Image to Image translation methods
Image to image translation (I2I) represents a fascinating group of methods that translate images from an input to an output. For example, the input can be an image of a summer landscape, and the output being images of how the same landscape could look during winter. Or the input image could be a medical image of a healthy patient, and the output shows how the image would look if the patient had some disease. Naturally, we expect that it should be a variation in how the output images should look. For example, the winter images can represent little snow or much snow, or the medical output images different stages of a disease.
IPFS for large-scale and long-term DNS data storage and access
Evaluate the use of IPFS for massive and large-scale DNS data storage and access.
Imputing Missing Data from Methylation Data for Cell Deconvolution Methods
DNA methylation is a major epigenetic modification of the human genome that affects fundamental biological functions, such as gene expression and cell development. In a simple language, this modification can be likened to a set of traffic lights.
Knowledge Graphs for Software Security Assessments and Cyber Threat Intelligence
Construction, evaluation and reasoning using knowledge graphs for software security assessments.
LLM-Driven Testing: Assessing Large Language Models in Cancer Registry Applications
This research project seeks to transform cancer registry testing by harnessing the power of Large Language Models (LLMs) like ChatGPT, offering automated, generative testing methods to detect anomalies, create test cases, and enhance data quality.
Malware in Deep Neural Networks
Deep learning systems are becoming increasingly popular, but their security aspects are often overlooked by developers. You will learn how deep neural networks can be used as an attack vector, and either explore how these attacks can be mitigated, or perhaps develop even more effective attacks.
Missing Data Imputation in Biology
This project aims to evaluate various imputation methods specifically for missing data in biological contexts. The student can also choose to examine the scalability of known methods or to develop new techniques that are scaleable.
Modeling the mechanics of the heart
The heart is the organ responsible for pumping blood around in your body. The heart consist of a tissue known as myocardium which is known to be a anisotropic, nonlinear, visco-elastic and nearly incompressible material. In order to create realistic models of the mechanics of the heart we would therefore need to incorporate these effects as well as appropriate boundary conditions.
Modelling and simulating the brain's waterscape
Monitoring the hygiene of non-routed space
Develop techniques to enable predictive capabilities in the detection and prevention of misuse of unallocated or allocated IP address space by malicious actors.
Monitoring the submarine cable network
Build an automated system for monitoring the deployment of submarine cables through large-scale passive and active measurements.
Next Generation Sport Systems - AI-Based Video Analysis, Processing, and Delivery
Interested in sport technology for videos used by elite leagues and live broadcasters? We have for long researched sports technology, both for distributing the video of live broadcasts and managing the archives in Norway and Sweden. We develop systems that are actively used and tested on real users, where the users can see highlights and summaries, get recommendations, collect favorite events, make personalized playlists, etc.
Optimizing Network Efficiency in Modern Supercomputers
How can we distribute workloads among compute nodes to make best use of the high speed networks in supercomputers?
PCI Express support for BeeGFS
In this master, you will add RDMA functionality to the global shared filesystem BeeGFS™️ (ThinkParQ) when running over PCIe NTB interconnects from Dolphin Interconnect Solution.
Parallel implementation of graph neural networks
Graph Neural Networks are a powerful machine learning technique for unstructured data. How can we make them scale to supercomputers?
Performance monitoring and diagnosis in mobile communication networks
Build an automated system based on machine learning to diagnose fault conditions in mobile communication networks.
Prediction of dry eye disease using metabolomics data
Development of methods to improve metabolomics data handling for predicting dry eye disease.
Privacy risk assessment in large-scale measurement datasets
Evaluate end-user and business privacy in public Internet measurement datasets.
Programing AMD GPUs for High performance Computing
AMD GPUs are very powerful, but how can we use their full potential?
Real-world optimization and machine learning on quantum computers
Up for quantum-powered solutions to real-world optimization or machine learning?
Semantic Parsing for Log Analysis, Fault Detection, and Fault Diagnosis
Develop and evaluate log parsing techniques for extraction of semantic information in log messages.
Sport news classification
Automatic classification of sport news into different categories using state of the art Natural Language Processing (NLP). The project is building upon an existing dataset of Norwegian soccer news.
Topic Classification of Scientific Papers
Millions of scientific papers are published very year. Can we use AI to understand their contents?
Towards Reliable Solutions from Artificial Intelligence: From Learning to Decision-Making
How can we use mathematical optimization, uncertainty quantification, and asymptotic statistics to develop a computational framework that takes us from data and training statistical models to actually making robust, risk-averse decisions based on the predictions of these models?
Unlocking the Potential of Digital Twins for Software Systems
Crafting digital twins for diverse software systems.