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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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to continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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world. We look forward to receiving your application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both