156 computational-physics "https:" "https:" "https:" "https:" "Trinity College, Dublin" uni jobs at Cardiff University
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medicinal chemistry and molecular biology techniques, including computer‑aided drug design (e.g., molecular dynamics and molecular docking), synthetic organic chemistry for compound synthesis, purification
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relevant, the desirable. As part of the application process you will be asked to provide this evidence via a supporting statement. Please ensure that the evidence you are providing corresponds with
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team members in welfare issues, escalating as necessary to specialist support areas. Provide support, feedback and pastoral care to students registered with the Career Confident programme. Ensure
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. In addition, the postholder will provide administrative support to the Pro-Dean for Education and Students, assisting with portfolio, programme, and process enhancements and initiatives. The role works
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interpretation of data from the UKRI NERC GRIM-SAF research programme, towards its publication in high-ranking international journal articles. This research program is conducted in partnership with
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, the desirable. As part of the application process you will be asked to provide this evidence via a supporting statement. Please ensure when submitting this document / attaching it to your application profile you
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managers to those team members). Play an influential role in bringing about a consistent approach to process and systems across colleges and service teams and in changing ways of working. Embed working
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cybercrime Computational criminology Social Network Analysis Policing, crime mapping and crime prevention International criminology and criminal justice Expertise in quantitative research methodology
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@cardiff.ac.uk Informal enquiries in relation to the recruitment / application process can be sent to Leah Fowler, HR Administrator – SHARE-HR@cardiff.ac.uk This post is full time (35 hours per week), is fixed
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advanced computer modelling (in silico), through robot driven testing of implanted knees (in vitro), to 3-dimensional X-ray imaging of moving patients (in vivo) with Machine Learning driven analysis