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Field
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-spotted spider mite, is among the most harmful pest insects worldwide. Due to its exceptional ability to adapt to various crops and pesticides, this mite serves as an excellent model system for studying
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preclinical experiments on ex vivo brain slices and in vivo rodent models to investigate and optimize the effects of TIS Analyze ex vivo and in vivo electrophysiological and fMRI imaging datasets Collaborate
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: the aluminum oxide layer is fully recyclable. As a PhD student, you will contribute to the development of multiphysics (electromagnetic & thermal) models for windings of electric motors. Also, you will study how
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, storage and demand. YOUR TASKS You will develop mathematical models and metaheuristic algorithms for complex optimization problems in the context described above, see e.g., https://arxiv.org/abs/2503.01325
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combined with an interdisciplinary toolbox drawing from ecology, forestry, and climatology. These data will then feed into cutting-edge joint species distribution models to project European forest plant
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nature and forests and a keen interest in forest ecology Keen to carry out fieldwork (Brazil, Belgium, UK and Australia) Previous experience with (terrestrial) laser scanning and 3D modelling is a plus
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wireless technologies. You implement the designed solutions on embedded hardware platforms and experimentally validate their performance. Experimental validation can be backed by modelling or theoretical
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. Defining predictive tasks based on clinical goals. Selecting and setting up appropriate data preprocessing pipelines. Training and evaluation of computer vision models. Internal and external algorithmic
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transduction mechanisms at the protein and membrane level. Experimental systems will include 2D cell culture, organoid models, and advanced biophysical assays (e.g., high-throughput atomic force microscopy
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) developing (new) GeoAI methods to enable fine-grained spatial data modelling of street environments, 2) elucidating the contribution of geographic environment to crimes and crime diversity, 3) large-scale