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Strong research background in statistical learning problems -- especially from a theoretical perspective -- as evidenced by publications Knowledge of MATLAB/R/Python Good written and oral
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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commonly used in genetics and molecular epidemiology. Expertise in R, Python, bash is required. Strong communication (oral and written) and interpersonal skills, and experience working in a large research
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and oral communication skills Proficiency in data analysis tools (e.g., R, Stata, Python, NVivo, GIS) Experience with interdisciplinary or community-based research is a plus How to apply: Interested
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cognitive experiments Expertise in both frequentist and Bayesian statistics performing descriptive and multivariate statistical analysis techniques such as SEM using SPSS and/or R language. Excellent ICT
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. Proven research ability as evidenced through a portfolio of publications and/or conference papers and/or patents. Strong background in R&D with self-motivation and initiative. Ability to develop and manage
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sciences, or other quantitative disciplines Demonstrates interest in global health and health policy Strong quantitative competencies and sound judgment in data analysis and interpretation Proficiency in R
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in mathematical modelling methods used to analyse infectious diseases • Strong programming skills, ideally with experience in R/R packages and GitHub as a collaborative coding environment • An ability
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, algorithmic, AI and communication skills. Proven track records of working in rapid R&D environment with wealth of experience in path planning for robotic arms, reinforcement deep learning and AI. Good
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analysis, preferably using software such as SPSS, R, MATLAB, or Python. Demonstrated ability to lead research projects, prepare manuscripts, and contribute to scholarly publications. Excellent written and