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statistical analysis of high-throughput sequencing data Candidates without a master’s degree have until 30 June 2024 to complete the final exam. Grade requirements: The norm is as follows: The average grade
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interdisciplinary teams Proven skills in statistical analysis and mathematical modelling tools Excellent knowledge of programming languages such as R, Python, etc. Familiarity with AI algorithms and machine learning
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skills in statistical analysis and mathematical modelling tools Excellent knowledge of programming languages such as R, Python, etc. Familiarity with AI algorithms and machine learning Excellent written
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dynamics Experience working in interdisciplinary teams Proven skills in statistical analysis and mathematical modelling tools Excellent knowledge of programming languages such as R, Python, etc. Familiarity
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, epigenetic and phenotypic analysis of plant reproduction Documented hands-on experience with bioinformatics and statistical analysis of high-throughput sequencing data Candidates without a master’s degree have
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experience in epidemiological modelling and infectious disease dynamics Experience working with social economic components in epidemic research. Proven skills in statistical analysis and mathematical modelling
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of their class with respect to academic credentials. Required: A general physics background on the master-level (MSc or equivalent) including statistical physics, hydrodynamics and soft condensed matter Foreign
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epidemic research. Proven skills in statistical analysis and mathematical modelling tools Excellent knowledge of programming languages such as R, Python, etc. Familiarity with AI algorithms and machine
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to develop implementation measures as part of the fellowship. The fellow is also expected to develop strong skills in advanced statistical methods and finalize three articles within the project period
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groundwater data and statistical/physically-based modelling background to join our team. The postdoc will analyse groundwater level observations and related climate and site-specific data to assess