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to assess stakeholder needs, create PV-integrated sensors to monitor agriculture-specific stressors, model stress impacts on PV performance, and develop innovative PV tracker controls. These elements will be
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monitoring of neurological diseases. While current diagnostic methods rely heavily on bulk measurements (e.g., cytokines, cell counts), they fail to capture the dynamic and heterogeneous nature of central
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work of the research group, it will include elements of method development within sample preparation, instrumental and data analysis, quality assurance/control and semi-quantification, in the context
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The candidate will be part of the MUNI-RISK and MMinE-SwEEPER consortia that will advance scientific understanding on risks of munition in the marine environment and develop methods for risk prioritizing and
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on understanding how different excitation methods generate polarons and correlated materials in the cuprates and other quantum materials, building on our recent results in the vanadium dioxide (see Johnson et al
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publication record relative to career stage Excellent written and spoken English communication skills Following qualifications will be considered as an advantage: Experience with explainable AI methods
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domain as documented by a PhD dissertation and/or research publications A solid theoretical and analytical understanding of text culture, AI, and related fields Experience of empirical research methods
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phenotype definitions and the genetic modeling such traits Develop methods to define climate breeding objectives Participate in project applications Collaborate with diverse group of researchers nationally
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candidate is expected to have high motivation and independence and good English communication skills as well as being a team player. Familiarity with openFoam and some experience in experimental methods is
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postdoctoral position in methods development and analysis of multi-omics and clinical data for inflammatory skin diseases. The start date is October 1st, 2025, or as soon as possible thereafter. Research context