56 ability "https:" "https:" "https:" "https:" "https:" "https:" positions in Ireland
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, and would have an ability to work on their own initiative as well as part of multi-agency team. A graduate qualification in an academic discipline relevant to public health or health services research
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) should be obtained from the Work at UCD website: https://www.ucd.ie/workatucd/jobs/ Where to apply Website https://www.aplitrak.com/?adid=eWFzZW1pbi5vemRlbWlyLjM0NTU1Ljk5MDhAdW5pY2R1Ymxp… Requirements
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spaces, data protection, privacy and digital trust frameworks. Where to apply Website https://www.mtu.ie/vacancies/ Requirements Research FieldComputer scienceEducation LevelPhD or equivalent Skills
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application procedure) should be obtained from the Work at UCD website: https://www.ucd.ie/workatucd/jobs/ Where to apply Website https://www.aplitrak.com/?adid=eWFzZW1pbi5vemRlbWlyLjIyNTI3Ljk5MDhAdW5pY2R1Ymxp
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conducted in accordance with Directive 2010/63/EU and S.I. No. 543 of 2012. The role requires excellent experimental, analytical, organisational and communication skills and the ability to work both
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2010/63/EU and S.I. No. 543 of 2012. The role requires excellent organisational, analytical and communication skills, along with the ability to work both independently and collaboratively within a
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, their ability to work on their own initiative and resolve problems. Closing Date: 12 noon (Irish Standard Time) 18th September 2026 Application will only be accepted by applying online through the Trinity Jobs
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apply and should refer to PD2 posts instead. Prior to application, further information (including application procedure) should be obtained from the Work at UCD website: https://www.ucd.ie/workatucd/jobs
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. Any CVs submitted by Recruitment Agencies will be returned. Prior to application, further information (including application procedure) should be obtained from the Work at UCD website: https
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future colliders. https://doi.org/10.1051/epjconf/201919901022 - Central exclusive production at LHCb https://arxiv.org/pdf/2507.13447 - Theory-Informed Neural Networks for Particle Physics Knowledge