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Field
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two main parts: • numerical modeling of crustal stresses associated with the various proposed mechanisms; • statistical analysis of the impact of these stresses on faults, potentially active or not, and
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of 20 per cent of full-time. Your qualifications You have gratuated at Master’s level in machine learning, computer science, statistics, or a related area that is considered relevant for the research
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Mathematics » Statistics Mathematics » Other Other Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract
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, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of 240 credits, at least 60 of which must
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of efficiency) Assessment of ease of implementation and interpretation in standard statistical software. Identification of minimum reporting items needed to make SMART methods transparent and reproducible
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alternative sources at time of application. The award will be taken up on 1 October 2026. Applicants must have a first degree in a relevant discipline e.g. public health, medical statistics, social sciences
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alternative sources at time of application. The award will be taken up on 1 October 2026. Applicants must have a first degree in a relevant discipline e.g. public health, medical statistics, social sciences
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ecology techniques (e.g. radio-tracking, animal handling, behavioural observation), geophysical and surveying techniques (e.g. ground-penetrating radar, relative GPS), working with big data, statistical
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equivalent, in bioinformatics, data science, computer science, computational biology, statistics, public health, biomedical engineering, applied mathematics, physics, or another quantitative field of relevance
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or signal analysis • Programming in Python or R • Statistical modelling Applicants without prior AI experience are encouraged to apply, as structured AI/ML training will be provided. Dairy Cattle Welfare