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these methods across different crops to identify conserved patterns of stress resilience 4. Identify candidate genes associated with key agronomic traits related to resilience 5. Contribute to software and web
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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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candidate will have an existing research profile that uses ethnographic methods to explore questions relevant to the social and ecological effects of livestock and agricultural infrastructures on landscapes
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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-novation, value creation, stakeholder engagement, and the development and validation of methods and tools for circular transitions. Your Profile We are looking for a candidate who has: A PhD degree in Design
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, C++, or equivalent) Experience with 3D modelling, rendering, or fabrication, and haptic feedback Familiarity with computational or optimization methods applied to interaction (e.g., computational UI
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of inorganic and coordination compounds, including discrete complexes and related systems, using methods such as X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM) and
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, including quantitative PTM analysis, mass spectrometry method development, data-independent acquisition (DIA) strategies, laboratory automation, and scalable proteomics workflows. Both positions are initially