Connectionists: PhD position in Machine Learning and Statistics

[filippo bianchi]

We are seeking a PhD Fellow that will:

   - Develop new statistical and machine learning methodologies to study
   dynamical processes that evolve both over time and space.
   - Apply the new methodologies to tackle energy analytics applications
   such as: energy load forecasting, anomalies and fault detection,
   optimization of the power flows on the energy grid.

The methodological part will focus on the design of models to process data
represented as time series and / or graphs to perform inference tasks, such
as prediction and classification.

Possible research directions are:

   - Develop Graph Neural Networks to model diffusion processes over a
   complex network or, in general, to predict the evolution of dynamically
   interacting systems;
   - Couple non-linear time series analysis and Bayesian inference to
   design novel Recurrent and Convolutional Neural Network architectures;
   - Exploit random matrix theory and matrix sampling to compute
   similarities between time series and graphs;
   - Develop deep learning architectures for graphs to solve combinatorial
   optimization problems, such as cluster assignment, set cover, and routing.

The PhD student will join the Statistics and Complex System Modeling groups
at the Mathematics and Statistics Department at UiT.

More information are available at:
https://www.jobbnorge.no/en/available-jobs/job/206271/phd-fellow-in-statistics-and-machine-learning
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