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Field
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with a strong data-driven or machine learning focus. Essential Skills & Experience Strong programming skills (Python essential) and experience with scientific computing libraries. Experience building
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experience performing experiments on fish or other animals. strong quantitative and data-handling skills (e.g. using R/python or an equivalent) certification in animal welfare (e.g. FELASA), although training
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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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and interpretation Proficiency in R or Python for statistical analysis and modelling Experience leading research teams or managing research projects (for Senior Research Fellow role) A solid publication
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researching the auditory midbrain (inferior colliculus). Detail-oriented, quantitative mindset and experience with scientific programming (Matlab, python, or R) is critical. Strong organizational, leadership
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, ICLR, ICCV, NeurIPS, AAAI, ECCV, MICCAI, IPMI, ICRA, IROS, RSS, and associated journals) Demonstrated ability to independently lead research projects Expertise with Python and relevant libraries (e.g
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
Engineering ii. Basic knowledge of Python iii. Basic CAD modeling skills acquired during academic training iv. Introductory familiarity with numerical simulation (e.g., finite element method and meshless
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user friendly scripts in R, and Python for data visualization and analysis that can be used by other team members. ● Creative problem solver with critical thinking skills and the ability to manage
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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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cross encoder architectures and retrieval augmented generation. Strong programming skills in Python and deep learning frameworks, familiarity with MLOps practices, data governance for sensitive health