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Field
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Experience at doctoral level or equivalent with a substantial research background, of strong relevance to the aims of INSTINCT-MB programme Clear evidence of the ability to work as part of a multi-disciplinary
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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State University of New York University at Albany | Albany, New York | United States | about 13 hours ago
on the adoption and use of information and technology by public organizations from a multi-disciplinary perspective and using a multimethod approach. It includes the study of areas characterized by social and
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sustainable interior environments. The curriculum emphasizes design at all scales from large-scale adaptive reuse to furniture design and fabrication. Students and faculty collaborate closely with faculty in
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(liquid nitrogen and/ helium atomic scale STEM Knowledge of in situ STEM characterisation Expertise in aberration corrected STEM What we can offer you: Grow your career: gain access to Imperial’s sector
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Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations, bibliometrics). Conduct data analysis using econometric and statistical
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scale. Assist/contribute in preparing research outputs including reports, publications, and presentations Work collaboratively across academic and policy networks The role offers an opportunity to work
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and
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applicants will be informed about the composition of the committee, and each applicant is given the opportunity to comment on the part of the assessment that concerns him/her self. Once the recruitment process
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hierarchies during cardiac, endothelial and hematopoietic development. Responsibility: * Develop or integrate novel statistical methods and algorithms for analyzing large-scale -omics data, including gene