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., Python, PyTorch). Practical experience in at least one of: driving simulation, spatiotemporal AI models, or traffic safety analysis. Strong publication record is an advantage. Excellent English
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• Proficiency in at least one statistical software (e.g., R, Stata, SPSS, Python) • Expertise in quantitative analysis, with preferred skills in o Quasi-experimental evaluation techniques (e.g., Difference-in
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statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in
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(e.g., LabArchives, BioRender). Desirable Demonstrated experience in fermentation control, optimisation and scale-up. Experience in the use of electronic lab books Experience in coding languages (Python
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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field - Strong quantitative and statistical background and related software and programming language proficiency (e.g., R, Python, Mplus, or similar) - Familiarity with big data, Linux systems, and remote
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Computer Science, Biomedical Engineering, Statistics, Biomedical Sciences, Electrical Engineering, or a related quantitative field Strong programming skills (e.g., Python, R, or similar) Demonstrated research
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statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in
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) Strong computational skills (e.g. coding in python, bash, matlab) Demonstrated aptitude for research Problem solving ability Ability to work in a multidisciplinary team Ability to work without close
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programming language (e.g., python, R, bash, perl) Experience in phylogenetic methods and algorithms Strong oral and written communication skills Ability to effectively collaborate and work with others Point