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2nd June 2024 Languages English English English Campus Ahus Postdoctoral fellowship in epidemiology /biostatistics/medical statistics Apply for this job See advertisement Job description Applicants
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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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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 Excellent
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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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defence are eligible for appointment. Demonstrated experience in epidemiological modelling and infectious disease dynamics Experience working in interdisciplinary teams Proven skills in statistical analysis
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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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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 Excellent
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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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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