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Hydrogen is expected to play a key role in the energy and fuel mix of future sustainable transport systems. However, due to its small and light molecular structure, hydrogen exhibits significantly
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build the sustainable companies and societies of the future. The signal processing group carries out research in the areas of inference and wireless communications, with both acoustic and radio signals
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ideas and influence the specific research focus within the broader research direction. The research direction will be two-fold: exploring the mixing capabilities to create mixed-metal nanoparticles with
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., multivariate statistics, linear and generalized linear mixed effects models, PCAs, PCoAs, RDAs, and permutational approaches) of large integrated datasets including ecological, environmental, molecular, and
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relevant subject. Experience working with statistical data analyses and mixed models. Merit: Knowledge of forage crop management. Knowledge of statistical analyses of experiments, mixed models, and
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powerful tool for studying biomolecular interactions, nanoparticle assembly, and other structural changes in real time. Microfluidic devices enable precise control over rapid mixing, optimised sample
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application! Work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning
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(e.g. in automotive applications) using, for example, vibration and acoustic signals. Research questions include method development for fault detection and isolation, damage estimation systems, including
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-performance organic mixed ionic-electronic conductors (OMIECs). An example of our recent work can be found here . You will work closely with fellow PhD students and postdocs at Chalmers, collaborating with
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USD). Project Description Beyond vision few studies have examined the role of light as signals to regulate development and function of different parts of the body. The identification of Opsin3 (Opn3