PhD Fellowship in Machine Learning: Join a dynamic research project at UiT The Arctic University of Norway, focused on advancing the state-of-the-art machine learning models for graphs and time series data. This PhD fellowship, funded by the Research Council of Norway, offers an exciting opportunity to delve into innovative techniques in deep learning and energy analytics.
PhD Fellowship in Machine Learning for Graphs and Time Series Data
Study Area: Machine Learning, Data Science, Computational Statistics, Energy Analytics
Location: Department of Mathematics and Statistics, UiT The Arctic University of Norway, Tromsø
Eligibility/Qualification:
- Master’s degree or equivalent in Computer Science, Electrical Engineering, Applied Mathematics, or related fields, with a focus on machine learning, data science, or computational statistics.
- Demonstrated interest or background in Artificial Intelligence/Machine Learning, particularly in deep learning for time series and graph data analysis.
- Proficiency in Python, familiarity with the Linux operating system, and common programming tools/environments (Git, SSH, Anaconda, etc.).
- Solid understanding of deep learning, experience with Pytorch, and common data analysis libraries such as Pandas, scikit-learn, Seaborn, etc.
- Proactive approach to learning new coding practices and frameworks, commitment to staying informed about cutting-edge developments in deep learning and related fields.
- Excellent written and verbal communication skills, with the ability to articulate complex concepts clearly.
- Ability to work in an international environment, fluency in English is mandatory.
Description: This PhD fellowship involves basic research in machine learning models for time series and graphs, focusing on advancing relational deep learning techniques. The project aims to develop innovative tools for processing spatio-temporal data, enhancing deep-learning models, modeling uncertainty, and ensuring interpretability. While energy analytics will be the primary field of application, the methodologies developed will have broader applicability.
How to Apply: Interested candidates must submit the following documents via Jobbnorge within the application deadline:
- Cover letter explaining motivation and research interests
- CV
- Diplomas for bachelor’s and master’s degrees
- Transcript of grades/academic record for bachelor’s and master’s degrees
- Explanation of the grading system for foreign education (Diploma Supplement if available)
- Documentation of English proficiency
- Three references with contact information, including the master thesis supervisor
- Master’s thesis and any other academic works
If you are near completion of your master’s degree, you may still apply and submit a draft version of the thesis along with a statement indicating when the degree will be obtained.
Last Date: May 2, 2024
Employer: UiT The Arctic University of Norway
Municipality: Tromsø – Romsa
This fellowship offers involvement in an exciting research project, good career opportunities, a supportive academic environment, flexible working hours, and a competitive salary. Join us in shaping the future of machine learning and energy analytics at UiT!
