Vacancies – AI Lab
Vacancies
Any vacancies at NTNU related to the AI lab will be listed here. The list is not automatically updated, but all vacancies at NTNU can be found here.
PhD Candidate within AI and Big Data
At the Department of Computer Science (IDI) there is a vacancy for a PhD Candidate within AI and Big Data.
The research of this position will be performed in cooperation with industry partners in the NorwAI research center, within one of the following topics:
- Topic A, dynamic graph analytics: For many important applications, data is represented as graphs, with dynamic relationships between nodes. Examples include the power grid, financial transaction relationships, social networks, and transportation networks. Interesting research challenges the PhD student might work on in this context include anomaly detection, scalable processing, and real-time processing of dynamic graphs.
- Topic B, streaming data: Data streams arrives with high velocity, possibly from multiple and heterogeneous streaming data sources. Examples of data streams we are working with are sensor data and customer interaction data. Often, there are strict requirements for processing time and resource usage, and interesting challenges here include developing efficient methods for analysing data streams under such constraints. Stream data can often have varying accuracy and quality, or have varying degree of completeness, how to handle this in the data processing and analysis part are also very relevant research topics.
- Topic C, efficient storage and processing of graph-based data: Data naturally modelled as graphs poses new challenges for efficient storage and querying. Relevant challenges for this topic include algorithms, indexes and systems for handling dynamic, streaming, or property graph data. Another relevant direction is to use AI to improve efficiency of graph database algorithms or database systems, for example using learned indexes.
Read more about the position and apply through Jobbnorge.
Application deadline: May 26 2024
Researcher in Robust AI
At the Department of Computer Science (IDI) there is a vacancy for a Researcher in Robust AI, fully funded over 18 months.
The position is connected to the project "RICO – Robust Intelligent Control", funded by the Norwegian Research Council and in collaboration between NTNU, SINTEF, ANEO A/S, SolutionSeeker A/S and Aneo Mobility. The ambition of RICO is to develop new methods for improved robustness of intelligent control systems and predictive models. The successful candidate will be working with particular emphasis on robustness in predictive systems. There is a gap between the research on very advanced methods tested in a controllable and predictable environment and the industrial setting where the environment is neither completely known nor controllable and where information can be scarce, uncertain and of low quality. This gap can be closed by developing novel methods for robust intelligent control and forecasting to be tested in both controlled environments and in industrial systems, and this is exactly the aim of RICO. RICO will consider a variety of use-cases, and the work developed by the successful candidate will be tested within these areas:
- Hydro-optimization for green energy production
- Energy load forecasting
- Charging of electrical vehicles
Read more about the position and apply through Jobbnorge.
Application deadline: June 1 2024
PhD Candidate in Interpretable Machine Learning (The LABDA Project)
At the Department of Computer Science (IDI) there is a vacancy for a PhD Candidate in Interpretable Machine Learning (The LABDA Project), within the Marie Skłodowska-Curie Doctoral Network ‘Interpretable machine learning for 24/7 movement behaviour’ funded by the European Union’s Horizon Europe research and innovation programme (grant agreement No.101072993). You will be hosted at the Department of Computer Science in the in the research unit Data and artificial intelligence (DART).
LABDA (Learning Network for Advanced Behavioural Data Analysis) is an EU-funded MSCA Doctoral Network, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research. The main aim of LABDA is to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk and facilitate the use and interpretability of the advanced methods for application in science, policy, and society. Via training- through-research projects, 13 doctoral fellows will establish novel methods for advanced 24/7 movement behaviour data analysis and assess the added value of linking multimodal data. Together, they will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. The open- source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. For more information, see the project’s website: labda-project.eu
Read more about the position and apply through Jobbnorge.
Application deadline: June 1 2024
Student engagements
Any student egagements will be posted here.
You can also find all student teaching assistant positions at the IE faculty on their website.