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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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databases and platforms. Support the implementation of functional studies and academic clinical trials in paediatric leukaemia. Facilitate communication and collaboration between national centres and
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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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reaction. This study will be carried out in collaboration with the CEA/IRFU and GANIL. The study focuses on proposals accepted at GANIL (https://www.ganil-spiral2.eu/ ) and Legnaro (https://www . lnl.infn.it
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collaboration with the Max Planck Institute for Plasma Physics (IPP) on the Wendelstein 7-X fusion reactor. You will be embedded in the Quantum and Terahertz Systems (QTS) group of the Department of Electrical
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Review Date: (To ensure consideration, please submit all application materials before review date) 03/13/2026 Posting Close Date Open Until Filled Yes Description of Work Recognizing that collaboration and
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with experimental and clinical collaborators. With the help of statistics, machine learning and pathway- and network- and analyses, the goal is to improve the mechanistic understanding of disease- and treatment
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Applications are open The PhD project is part of a collaborative project: ‘A “virtual market” for analysing the uptake of energy efficiency measures in residential and commercial sectors’, funded by
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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learning). The project will be carried out in a collaboration between Linköping University (main supervisor: Anders Eklund) and Lund University (co-supervisor: Mikael Nilsson) through an ELLIIT collaborative