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systems, statistical physics and machine learning, and using these insights to develop new methods, with the support of competent and friendly colleagues in an international environment? Are you looking
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information, is multitasking. However, we are notoriously poor multitaskers. A recent framework proposes that this is due to the fact that tasks often share representations, which can be beneficial for learning
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
of the proposed research subject : A state of the art, bibliography and scientific references are available at the following URL: https://vincentgaudilliere.github.io/files
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differential equation models of bacterial persistence. A particular challenge, both for simulation and for machine learning, lies in the high dimensionality of these equations, which causes grid-based numerical
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& Solar Power Plants Battery Energy Storage Systems (BESS) Smart Grids & Power Systems Sustainable buildings, communities and cities[EC1.1] Artificial Intelligence & Machine Learning for Energy Digital
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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the project’s research area Training in higher education teaching and learning Experience teaching Swedish and European economic history Documented administrative ability Assessment criteria The School
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dissertation Where to apply Website https://www.academictransfer.com/en/jobs/360066/phd-candidate-improving-gene-va… Requirements Specific Requirements Profile Candidates must hold a Master's degree in Molecular
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reside in France for the duration of the doctoral contract. Proficiency in or the ability to learn French is essential. The dissertation may be written in English, but it must be accompanied by an abstract
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through