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Field
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storage potential and future carbon removal and carbon farming policy scenarios. It integrates process-based knowledge with machine learning and especially aims to spatially quantify uncertainties. You will
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-enhanced Raman spectroscopy down angstrom spatial resolution. In this position you will: work as part of a team conducting basic research that advances our understanding of quantum materials conduct
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for predictive and prescriptive urban data analysis. Experience in visualizing and analyzing spatial and functional interactions within urban infrastructure. Personal and Organizational Skills Ability to model
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collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other requirements include: Strong background in communication theory
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academic qualifications at the PhD level in bioinformatics, computational biology, biomedical sciences or a related field. Documented expertise in scRNA-seq analysis and familiarity with spatial
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Preferred Qualifications : Competitive candidates should be highly motivated and possess a PhD in quantitative/spatial ecology of wildlife or a related discipline with a strong quantitative emphasis (ABD
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with R, statistical and spatial analysis Excellent communication and teamwork skills Excellent organizational skills Experience with field work in post-disturbance forest conditions (desired) Experience
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methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based methods. As part of a
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, RNA seq, single cell sequencing, spatial transcriptomics), bioinformatics tools and human studies. Your profile: PhD in bioinformatics, oncology or computational biology profound experience in system
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or spatial profiling; Mouse genetics and in vivo experimentation; Lineage tracing, clonal dynamics, or immune repertoire studies Candidates should hold (or be close to completing) a PhD in a relevant field