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PhD Candidate Adsorption Energy Distributions in Ion Chromatography 100 %

ZHAW

Suiza📅 17/10/2026

Descripción

Help advance the fundamental understanding of ion chromatography by developing innovative methods to characterize chromatographic materials and improve analytical performance. PhD Candidate Adsorption Energy Distributions in Ion Chromatography 100 % \*\*School:\*\* Life Sciences und Facility Management \*\*Starting date:\*\* Upon agreement. Fixed\\-term position (4 years). \*\*Your role\*\* As part of this PhD project, you will investigate the energetic heterogeneity of ion chromatography stationary phases and contribute to the development of new characterization methodologies. Your work will combine advanced chromatographic experiments with data analysis and modelling. You will: \* Develop and establish methodologies for determining adsorption energy distributions in ion chromatography. \* Perform adsorption isotherm measurements and chromatographic experiments using state\\-of\\-the\\-art analytical instrumentation. \* Investigate the relationship between stationary phase properties, adsorption energetics and peak shape characteristics. \* Develop and validate predictive models for chromatographic performance. \* Publish your findings in international peer\\-reviewed journals and present your work at scientific conferences. \* Contribute to collaborations with academic and industrial project partners. \*\*Your profile\*\* You hold a Master's degree in Chemistry, Analytical Chemistry, Chemical Engineering, Materials Science or a related discipline and are enthusiastic about scientific research. In addition, you bring: \* A strong interest in chromatography, separation science and physico\\-chemical processes. \* The ability to work independently while contributing effectively to a collaborative research environment. \* Excellent written and spoken English skills. \* Motivation to communicate scientific results through publications and conference presentations. Experience in chromatography, mathematical modelling or advanced data analysis is considered an advantage
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