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your points for admission. Emphasis is also placed on your: strong background in quantitative analysis and data manipulation proficiency with programming languages such as R, Python, MATLAB, or similar
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of the Research Grant Holder Statute. Preferential factors: Previous training in Bioinformatics. Previous knowledge and/or experience in programming, preferentially on Python language. Knowledge on the area of
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experiments Experience with robotics/autonomous systems Knowledge about marine technology Norwegian oral and written language skills Excellent programming skills (MATLAB, Python etc.) Personal characteristics
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, generative models, or equation discovery — rather than traditional statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch
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, Python, Java) and good communication, interpersonal, and problem-solving skills are also required for this role. This role meets the eligibility requirements for a skilled worker certificate of sponsorship
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imaging hardware, as well as programming skills in Matlab, LabView, Python, or a C-based language. Experience in neuroscience research and evidence of creative, original work in hardware or software
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University of California, Los Angeles | Los Angeles, California | United States | about 19 hours ago
eager to develop engineering skills. • Experience with real data analysis in R or Python; familiarity with health, biomedical, or sensor data is welcome but not required. • Excellent written and oral
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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