Manually tagging and processing video is time consuming. Here, we want to automate the task of generating events and summaries, like the clips shown in the sport news, using for example machine learning.
Manually tagging and processing video is time consuming. Here, we want to automate the task of generating events and summaries, like the clips shown in the sport news, using for example machine learning.
You will implement and test a web-based design interface, where exercise managers and trainers can plan crisis response exercises. This front-end will interact with an AI-planning module (developed by us) which aids planners when using the front-end. The planned simulation will run in a simulation platform such as Unity.
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.
Interested in sports? Technology used by real athletes? Analysing wellness and training load data is important to avoid injuries and increase performance. Here, the aim is to build a system to automatically analyse and predict outcomes ...
Interested in sports? Technology used by real athletes? Analysing wellness and training load data is important to avoid injuries and increase performance. Here, the aim is to build a system to automatically analyse and predict outcomes ...
Applying evolutionary algorithms to automatically generating effective Java performance tests with EvoSuite, a state-of-the-art test generation tool
Applying genetic algorithms to reorder microbenchmark suites to find larger performance changes sooner
This topic focuses on developing automated testing methods for cancer registration and support system at Cancer Registry of Norway.
Developing automated testing tools for telehealth services at Oslo City.
The proposed project aims to revolutionise the way Child Protective Services and Law enforcement personnel are trained to conduct interviews of children. The idea is to build an avatar assisting the training and giving feedback on questions asked with help of novel explainable and interpretable artificial intelligence (AI) methods.
Applying novel AI-based techniques to synthesize realistic cancer patient data for automated testing of the Cancer Registry of Norway systems
Develop and evaluate methods for automated synthesis of correct, gas-minimized, repairs for vulnerable smart contracts
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.
Develop and evaluate data-driven techniques and prototypes for automatically repairing security vulnerabilities in source code.
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.
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.
This project focuses on building digital twins for Smart Buildings (e.g., Smart Hospitals) and Smart Power Generators (e.g., Wind Turbines) for advanced analyses with AI techniques.
For future cancer patients, the accurate detection of early cancer signals could be life-saving, allowing for early and effective treatments to prevent cancer formation. In this project, you will have the opportunity to look for early signals of cancer, using a longitudinal machine learning technique.
The data from the online social network Reddit can be accessed in its entirety. We have developed a framework to calculate weighted relationships between subreddits. The graph of the relationships between all subreddits is divided into time slices and will be examined in this thesis.
How can we divide massive data streams meaningful classes without accessing the full dataset ? In this thesis we will explore clustering algorithms that can deal with data as it is scraped from social networks.
This project compares various available quantum computer emulators and see how their correctness varies.
Develop and evaluate data-driven techniques and prototypes for automatically assessing the security of a software system by analyzing the system's source code for potential security vulnerabilities during the development stage.
To discover defects and uncertainties in the deep learning models and input datasets to increase the quality and reliability of the prediction performance.
This project involves developing new methods to design, develop, and test Autonomous Rovers, similar to those used on Mars.
Over the recent years Generative Adversarial Models (GANs) have revolutionized the field of artificial intelligence (AI). Given a set of training data, the models can generate outcomes with striking similarities in properties to the training data. E.g. if the training data is a set of images, the models are able to generate new images with the same properties and look stunningly realistic. However the theoretical foundation of GANs is still immature and further understanding is needed. For example, GANs have been criticised to possibly only replicate a limited set of the properties in the training data.
Implementing software tools for designing, developing, and testing of self-driving cars.
This topic is about developing digital twins for various types of cyber-physical systems.
Network science has given us fundamental new insights into the structure of complex networks such as online social networks. Our goal is to expand these techniques to analyze social networks while they grow.
Enable high-level programming for Graphcore’s IPU Machine Learning hardware accelerator. The goal is to enable high-level Julialang-based IPU development without forcing a user to handcraft assembly or C++.
Are you interested in experimenting with IBM's real quantum computer?
Explainable Artificial Intelligence (XAI) represent different techniques to be able to interpret opaque machine learning models and predictions. This is important to improve the reliability and thrust in machine learning methods.
Explain the prediction of a deep neural network analyzing microscopic videos of human semen.
Unravel the effect of SK channel and its pharmacological block in health and atrial fibrillation, using computational models at different levels of the heart.
This topic explores the potential of using synthetic data to train machine learning algorithms in fields that don’t have a lot of open annotated datasets, such as medicine.
This master project will investigate novel use of programmable and re-configurable network adaptors for efficiently enabling data consistency which is vital for the performance of modern distributed data storage.
Applying Deep Neural Networks for the generation of synthetic health data that protects privacy and promotes healthcare research
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.
How can we predict traffic in connected systems such as road networks, and how can we use these predictions to place charging stations for electrical vehicles ? Graph neural networks are currently the most promising approach for studying these question.
We collected large amounts of data from the online social network Twitter and reconstructed the underlying interaction network. This thesis aims to develop and implement a for information diffusion, which we plan to apply on misinformation and conspiracy data.
Construction, evaluation and reasoning using knowledge graphs for software vulnerability assessments.
Safety in Deep Reinforcement Learning is a challenging task, because the learned strategy can not easily be inspected. We want to learn an additional safety model, based on logic constraints.
The Graphcore IPU is a novel AI processor that is radically different from traditional CPUs and GPUs. It offers very high memory performance, but using it requires a redesign of existing algorithms, which is topic of this thesis.
Studying mutation testing on real-world Go programs and devising new mutation operators for message-passing concurrency
Manually tagging social media posts is time-consuming, especially when the data sets grow in the billions. We aim to develop a machine learning-based system that automates the detection of conspiracy-supporting or promoting tweets collected during the COVID-19 pandemic.
In this project we will explore techniques to estimate the parameters of a model, when the likelihood or loss function cannot be computed. A popular framework is approximate Bayesian computing (ABC), and we will explore new estimation methods within this framework.
The goal is to apply existing quantum search algorithms or develop new quantum search and optimization algorithms to solve classical optimization problems.
This thesis topic focuses on the methods to develop and test quantum programs.
Quantiles are fundamental in statistics and machine learning. In this project we will explore new algorithms that are able to estimate quantiles in real-time.
Forecasts of precipitation the next minutes and hours ahead are of great value for a wide range of users including the public. At the Norwegian Meteorological Institute such forecasts have been generated for several years using optical flow methods and made available in the yr app and at yr.no. In recent years approaches based on deep learning have also demonstrated promising results. For example, the use of convolutional LSTMs has been proposed by Shi et. al. (2015), while Sønderby et. al. (2020) apply networks with attention-based layers.
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.
Time series analysis using Residual neural networks. The main task is to explore how different ResNets can be combined to solve complex time series problems.
Developing methods based on AI techniques to discover unforeseen situations in Elevators.
We will explore generative adversarial network models(GANs) to capture spatial dependencies in forecasting uncertainty, e.g. when predicting the next frame in a video or the weather tomorrow.