Filter by research area:
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.
We are developing an AI child avatar for the interview training of the police officers to protect vulnerable children from abuse.
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.
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.
Can we accurately measure the 5G network performance using less data than the standard tools?
Building a machine learning model using data plane programming language such as P4 that detect network security attacks at line rate with high accuracy
Quantifying the real-life effect of 5G deployment based on Simula's own measurements.
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 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.
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.
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 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.
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.
Evaluate the use of IPFS for massive and large-scale DNS data storage and access.
Construction, evaluation and reasoning using knowledge graphs for software security assessments.
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.
In this project we will use numerical simulations (finite element method) to understand fluid patterns in and around the brain. You will work with detailed meshes of the human brain and investigate different theories on fluid flow and transport in the brain related to diseases such as Alzheimer's and Hydrocephalus.
Missing data occurs a lot in practice: images that lack a piece, people don't fill in all values in a survey, and broken sensors lead to unavailable data,... You will try to fill in the missing entries with reasonable values or make a new model that can handle missing data directly
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.
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.
Develop techniques to enable predictive capabilities in the detection and prevention of misuse of unallocated or allocated IP address space by malicious actors.
Build an automated system for monitoring the deployment of submarine cables through large-scale passive and active measurements.
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.
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.
Build an automated system based on machine learning to diagnose fault conditions in mobile communication networks.
Development of methods to improve metabolomics data handling for predicting dry eye disease.
Evaluate end-user and business privacy in public Internet measurement datasets.
Up for quantum-powered solutions to real-world optimization or machine learning?
Develop and evaluate log parsing techniques for extraction of semantic information in log messages.
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.
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?
Crafting digital twins for diverse software systems.