79 structural-engineering-phd "https:" "IMT Atlantique" Postdoctoral positions in Brazil
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describing their progress and activities. Mandatory requirements Candidates must hold a PhD in Mathematics Education or a closely related field by the start of the fellowship. How to apply Applicants must
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7 Feb 2026 Job Information Organisation/Company FAPESP - São Paulo Research Foundation Research Field Engineering Researcher Profile Established Researcher (R3) Application Deadline 25 Feb 2026 - 23
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. Mandatory requirements: PhD obtained within the last seven years in a field related to the project; availability to start immediately; prior publications related to the project; proven skills in MariaDB
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expenses Duration: 36 months, with the possibility of an international exchange period Workload: Full-time (40 hours per week) Expected start date: April 2026 Requirements: • PhD in Chemical Engineering
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different subtypes; • Spatial transcriptomics. Mandatory requirements PhD in areas related to the project. Desirable requirements Candidates should demonstrate experience in: In vitro and in vivo experimental
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spectrum of artificial intelligence in its various interfaces), the failure of liberal democracies, and the constitution of contemporary subjectivities exiled from themselves. Mandatory requirements - PhD in
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and activities. Mandatory requirements Candidates must hold a PhD in Geometry or a closely related field by the start of the fellowship. Potential supervisors: - Algebraic Geometry: Marcos Jardim, Ethan
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(δ¹³C) with continuous monitoring of microclimatic data (soil and air temperature and humidity) to quantify ecosystem responses to anthropogenic disturbances. The proposal is structured around 3
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, including the use of resources such as audio description, tactile materials, and assistive technologies. Mandatory requirements: Applicants must have completed their PhD within the last seven years and be
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness