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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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-scale effect. The end goal of this project is to develop, via experimental testing and advanced material characterization, quantitative models of the process underlying the repeated generation
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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disorders, their treatment, and outcomes. The Centre has access to a large database of biomarkers, clinical, and cognitive variables, genotyping and sequencing data, and MRI brain imaging data on patients
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process and should be well documented in a teaching portfolio. The applicant should describe her/his qualifications in view of the Scholarship of Teaching and Learning (SoTL) framework which includes: Focus
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-FATES model using: Snow cover Flux tower data The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently
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and final assessment of competencies and milestones. Candidates will also be evaluated on personal suitability and ability to contribute to academic citizenship, both during the initial hiring process
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological