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, regulation and programs to achieve their energy goals Direct the design and application of sophisticated analytical methods, and direct analysis of data as required for in-depth evaluation of technical and
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, analytical chemistry, biochemistry. Proficiency with scientific software and common lab equipment is advantageous. Previous laboratory experience is beneficial but not mandatory. We are seeking highly
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emphasis will be placed on scientific excellence. Merits include documented experience in topological phases in moiré materials/2D systems, strongly correlated physics, and relevant analytical or numerical
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image analysisExcellent organizational and analytical skillsAbility to work independently and collaboratively in an interdisciplinary environment Language Requirements: Applicants must demonstrate
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; Experience competing for external funding; Experience publishing research in high-impact, refereed journals; Analytical skills; Written and oral communication skills; Experience with strategic planning, risk
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) publications in international journals indexed in Web of Science (Clarivate Analytics) within the scientific area of the competition, not counting publications that do not fall within the scientific area of
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management and analytical tools. Experience with survey design. Ability to work collaboratively in teams. Preferred Qualifications Ph.D. or equivalent with post-doctoral research experience in social sciences
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. Documented experience with analytical methods and wet-lab experimentation. University-level teaching and supervision experience. Track record of scientific publications in high-quality journals. Strong
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an interdisciplinary research team; Excellent publication records; Experience in large-scale earth observation analytics; Experience in integrating multi-source remote sensing data with process-based or data-driven
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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning