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duration of employment This is a 5-month position (30 hours/week) from 01 November 2026. Job description You will be contributing to the development of catalytic methods based on transition metal catalysis
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computer science and philosophy. The REMAX project on Rigorous Evaluation Methods for AI Explainability, funded by a Villum Synergy grant, aims to create methods for explainable AI and their rigorous evaluation. It
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their association with mobile genetic elements with the aim of devising a preliminary framework for risk factor analysis related to persistence of AMR, horizontal gene transfer, and dissemination between bacterial
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materials. Use molecular dynamics, lattice-dynamical methods and statistical sampling to investigate local disorder, phase stability, temperature-dependent behaviour and structure-property relations
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embeddings, and developing explainable AI methods that translate model outputs into interpretable visualisations for clinicians and chemists. Extending methods to high-resolution MALDI (MALDI-2, timsTOF) and
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, or other participatory research methods is welcome but not required. Experience with food consumption, sustainability, or circular economy research is an advantage but not a requirement. Note that you do not
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already have used data-driven computational methods to model cognitive or behavioural change in any substantive domain, that would be ideal. Experience specifically with research on consumers or citizens
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; survey design and online experimental methods; quantitative data analysis, preferably including choice modelling, willingness-to-pay analysis, segmentation, multivariate statistics, or related methods
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evolutionary genomics and population genetics, with demonstrated experience applying evolutionary-genomic methods to whole-genome data. Extensive hands-on experience with bioinformatic analysis of whole-genome
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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines