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College Cork (UCC) is looking for two highly motivated Post-Doctoral Researchers. The researchers will be employed on a technology development project in which UCC collaborates with two companies, Danalto
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technology development project in which UCC collaborates with two companies, Danalto and Qorvo. The UCC part of the project focuses on the development of agentic AI and digital twin concepts for Ultra-Wideband
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Post Doctoral Researcher / Senior Post Doctoral Researcher - School of Biochemistry and Cell Biology
-seq, tRNA-seq, scRibo-seq) to generate high-quality data from ovarian cancer cell lines, organoids, and clinical samples. Through close collaboration with the bioinformatics team and industry partner
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Post Doctoral Researcher / Senior Post Doctoral Researcher - School of Biochemistry and Cell Biology
, such as fusion gene identification and somatic short-variant calling and interpretation. Through close collaboration with wet-lab scientists and the industry partner, the candidate will build robust
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well as part of a team. They will be expected to contribute to scientific publications, conference presentations, grant applications and dissemination activities, while working within a highly collaborative
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is led by University College Cork with partners Exceedence Ltd and Wind Energy Ireland, and with Flotation Energy, Simply Blue Group, ESB and the Marine Renewables Industry Association as collaborators
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/communications, machine learning/vision, intelligent transportation systems, intelligent sensing/localization, or signal processing. In line with our Athena SWAN ambitions we especially encourage women to apply
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dissemination activities, while working within a highly collaborative multidisciplinary environment. Please note that this role may be extended by 6 months, pending approval. Please note that Garda vetting and/or
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experimental, analytical, organisational and communication skills and the ability to work both independently and collaboratively within a multidisciplinary research environment. UCC is an accredited research
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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics