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between government actors (e.g., county councils) and communities to determine the local development potential and reception of renewable energy projects in coastal areas, in line with key government policy
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of the applicant will be either computational biology, wet-lab or a hybrid of both depending on the successful candidates expertise. There will be strong opportunities for continued professional development as part
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for complex geological properties. Collection of geochemical and petrological reference data to train models is an equally important component of this task. The post will involve spectral, petrophysical, and
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Investigator. The primary purpose of the role is to deliver research results and objectives, develop new or advanced research skills and competencies, the successful development of funding proposals and to
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on ensuring transparency through citation of source material and developing methods to quantify statistical uncertainty in generated outputs. The post-holder will collaborate with a multidisciplinary team
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of the work packages of the project, as detailed below. They will have the opportunity to develop their research, both individually and as part of a team of ambitious scholars. Postdoctoral Researcher (2026
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have the opportunity to develop their research, both individually and as part of a team of ambitious scholars. Salary: Post-Doctoral Researcher, Level 1 (2026): €47,273 p.a. (Point 1 - with increment
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the activities of the project, contribute to the publications ensuing from them, and co-lead with the PI on key work packages of the project. They will have the opportunity to develop their research, both
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Post Doctoral Researcher / Senior Post Doctoral Researcher - School of Biochemistry and Cell Biology
partner. The successful candidates will work at the interface of academia and industry, leading research on developing, implementing, and optimizing cutting-edge translatomics techniques (Ribo-seq, Disome
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the UCD School of Mathematics and Statistics. The successful candidate will lead research focused on developing a scalable, AI-driven quality control (QC) system for synoptic and climate observation data