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: The doctoral candidate will perform computational analysis of a combination of multi-omics data from the gut microbiomes of patients with Alzheimer's disease or Parkinson's disease. The candidate will use state
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health tools. Basic proficiency in data analysis (Python or R); experience with speech analysis libraries or NLP is an asset. Strong scientific writing skills and a collaborative spirit. High motivation
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methods for single-cell data analysis (tools developed by the team : https://github.com/cantinilab ). Single-cell high-throughput sequencing, extracting huge amounts molecular data from a cell, is creating
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, traditional risk prediction models like the Steno Type 1 Risk Engine fail to account for the immunological dysregulation inherent in T1D. Project Objective The PhD candidate will primarily focus on the clinical
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more (for an overview of these technologies, see http://behaverse.org/ ). Your role in this team will be to develop computational models and data analysis code to process large, multimodal behavioral
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and neural computation. By leveraging the rich data repository on the larva’s neural connectome and muscular structure, the project seeks to create accurate simulations that can inform biological
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existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the PhD project is encouraged. The successful candidate will be affiliated with the Institute