Connectionists: Fully funded 4-year PhD positions in Machine Learning for Medical Image Analysis at UiT The Arctic University of Norway

[Michael Kampffmeyer]

Please see below for details about an open call for 3-5 4-year PhD positions at UiT The Arctic University of Norway.

We, the Machine Learning Group at UiT (http://machine-learning.uit.no), are seeking PhD Candidates to take an active role in the group's research on developing novel machine learning methodology for 1) medical image analysis or for 2) integration of renewable energy in smart power grids.
Research projects proposed by the Machine Learning group are:

1) Next generation medical computer vision
Learning from limited labels is a fundamental challenge in machine learning, especially in the medical domain. This project develops deep learning based algorithms for zero-shot learning, few-shot learning, clustering and domain adaptation and is connected to the joint effort of the UiT Machine Learning Group and the University Hospital of North Norway and the establishment of the Center for Patient-Centered AI. Applicants should have a machine learning or deep learning background and a solid Mathematics background.

2) Deep learning for integration of renewable energy in smart power grids
Distributed generation of solar and wind power are important parts of smart power systems to supply rural communities with clean energy and avoid investments to increase grid capacity. Reliable integration of intermittent energy sources requires exact predictions of consumer demand, available power, and the impact on load flow and voltage stability. Applicants must have solid experience with machine learning theory and Python frameworks for deep learning.

For more information about the application procedure, salary, etc, please visit
https://www.jobbnorge.no/en/available-jobs/job/186596/3-5-phd-fellows-in-physics-and-technology

The successful candidate(s) will join the UiT Machine Learning Group, a vibrant group at the "north pole", with excellent national and international connections.

Best regards,
Michael Kampffmeyer
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