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monitoring will also be integrated into this PhD. Thermal prediction models are currently implemented on a Field Programmable Gate Array (FPGA), while the thermal PI controller (which will be further developed
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. Strong knowledge of quantitative and/or computational research methods, ideally in econometric analysis or optimization and simulation models. Preferable knowledge in Python and STATA. A collaborative team
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To analyse tick abundance data To collect and analyse tick abundance data in relation to forest management in the context of climate change To apply an existing population dynamic model to our study cases
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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. The PhD student will focus on characterizing immune cell responses in food allergy models and their impact on brain immunity. In close collaboration with experts in food allergy, neuroimmunology, and
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high-dimensional settings). Furthermore, when developing appropriate statistical methods, one should avoid making unrealistic (too restrictive) assumptions. This calls for flexible modelling and
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model and patient samples. Job requirements Essential Requirements: Master's degree in Biochemistry, Cell Biology, Molecular or Structural Biology, Biological/Medical Sciences, or a closely related field
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captures a variety of soil conditions coupled to a modelling approach. In that context, the PhD will focus on the following issues: assessing the impacts of soil conditions and stand type (unconverted
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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. These studies will initially be performed in tractable cell lines to leverage insights gained in the molecular mechanism of protein complex formation, before moving into an iPSC-derived neuronal model and patient