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experience in analysis of large microbial genomic datasets and design and validation of machine learning models. Metagenomics experience is an advantage. You have proficiency with programming languages e.g
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), and machine learning techniques to identify new players of plant growth during and after an immune response. You will validate the genetic leads, such as transcription factors (TFs), identified in our
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(e.g. R, Matlab, Python); some experience with (OMICs) data analysis and/or Chemometrics and/or machine-learning, or willing to learn; a strong motivation to work in a multidisciplinary international
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Center (NPEC) . You will utilise modelling, genetic mapping, and machine learning techniques to identify genes controlling the growth during and after an immune response. In the second phase of the project
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The rapidly evolving field of AI offers promising solutions to the literature screening challenge using machine learning models, like active learning, and, very recently, large language models (LLMs). However
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to the literature screening challenge using machine learning models, like active learning, and, very recently, large language models (LLMs). However, many of these AI-driven solutions emerge from tech companies
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skills in python; solid knowledge of machine learning (including deep learning) and good background in formal linguistics; high proficiency in academic English; ability to work both as an independent
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the application of rare event techniques to tipping in ESMs. One key goal here is to develop techniques to efficiently determine transition pathways using advanced Machine Learning techniques. Additionally
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staff position within a Research Infrastructure? No Offer Description Ready to make a real difference? Are you interested in applying cutting-edge statistical and machine learning techniques to develop