Madame Rachel EGLETON / PhD student

Skills

Throughout my academic training, I have developed strong cross-disciplinary and interpersonal skills relevant to collaborative research environments. I have gained experience in teamwork and project coordination during multiple research projects, often working in new groups to design sampling strategies, process datasets and interpret environmental archives. My MSc work has strengthened my problem-solving abilities through the analysis of complex geochemical datasets and the use of statistical and modelling tools. I have developed effective time-management skills by running parallel laboratory, computational and writing tasks under fixed deadlines. I have experience communicating scientific results through written reports and oral presentations, as well as supervising undergraduate students as a demonstrator. My laboratory experience (micro-milling, ICP-OES, stable isotope mass spectrometry) has reinforced my attention to detail, data quality control and methodological rigour. My coding experience in R and Python has improved my ability to learn new technical skills quickly and adapt analytical workflows. Together, these experiences have equipped me with the adaptability, critical thinking and collaborative mindset required for interdisciplinary doctoral research.

Techniques

Geochemistry & laboratory techniques: Micro-milling of carbonates; ICP-OES analysis; LA-ICP-MS; stable isotope ratio mass spectrometry (δ¹⁸O, δ¹³C); U–Th dating; petrographic and sedimentological analysis; XRF (core scanning and discrete analysis); Loss-on-Ignition; particle size analysis. Carbonate archives & paleoclimate reconstruction: Stable isotope interpretation; trace-element climatology; laminae analysis; age-depth modelling (COPRA, ITRAX); integration of multi-proxy datasets; Holocene and Quaternary climate reconstruction. Hydroclimate & environmental modelling: Experience with Century and DayCent biogeochemical climate models; application of environmental proxies to hydrological and climatic variability. Data analysis & coding: Dynamic Time Warping, Principle Component Analysis, Spectral and Wavelet analysis; R and Python for data processing and visualization; Stata for statistical analysis; ArcGIS and ArcGIS Pro for spatial analysis; creation of open-access datasets.