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pangenomics of polyploids Project description This PhD project investigates how whole-genome duplication reshapes genome evolution using comparative pangenomics across multiple natural diploid–polyploid species
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across multiple data modalities Manage HPC resources and job scheduling on NAISS Arrhenius CPU and GPU partitions Requirements To meet the entry requirements for doctoral studies, you must hold a Master’s
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diagnosis is often challenging for patients presenting with vague, non-specific symptoms that may be linked to multiple cancer sites. This project aims to improve diagnostic decision-making in such patients
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(eDNA) approaches for biodiversity assessment across multiple habitat types. The PhD project will focus on applying and developing eDNA‑based methods to assess biodiversity and ecological communities in
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allocation, or control optimization). Experience with 3D sensing, perception, computer vision, or sensor fusion. Proficiency in programming languages such as Python, C++, or C#. Experience working with
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and applying high-resolution time-lapse GPR and EMI imaging methods at multiple scales to enhance our understanding of subsurface flow Designing and implementing novel inversion algorithms for GPR and
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, including, but not restricted to, geometry or shape optimization, parameter optimization, or multi-objective optimization. · Strong programming skills (e.g. Python, C/C++, R or similar) and experience
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trigger redesigns across multiple groups. The challenge is compounded by the fact that each discipline uses different data models and representations, making system-level interdependencies difficult
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how multiple impacts, at different spatial and temporal scales, interact to transform the overall functioning of ecosystems using the resilience pivots method: the accumulation of impacts is analysed in
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chemical exchange saturation transfer. By combining multiple modalities and parameters, we aim to identify at risk sites for GBM relapse at the earliest opportunity, before progression becomes apparent