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» Computer engineering Researcher Profile First Stage Researcher (R1) Positions Other Positions Application Deadline 20 Oct 2026 - 17:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full
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strong understanding of core machine learning concepts as well as experience training testing and validating machine learning models across a variety of platforms. Duties/Responsibilities: (40%) Dataset
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focus primarily on computational modelling of infant behaviour and experience, using approaches from machine learning, unsupervised learning, dynamical systems, information theory, or related quantitative
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, machine learning, and statistical methods for time-series forecasting in the electricity sector, including demand, renewable generation, and market prices. Development and evaluation of point and
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Texas A&M Engineering Extension Service TEEX | College Station, Texas | United States | about 5 hours ago
, cybersecurity, economic, and workforce development, and homeland security. Every TEEX employee has a direct impact on Making a Difference to the professionals who respond to emergencies, safeguard communities
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Texas A&M Engineering Extension Service TEEX | Brownsville, Texas | United States | about 5 hours ago
Service’s (TEEX) Safety, Teamwork, Adaptability, Respect and Stewardship (STARS) values, is instrumental in making a difference for the Infrastructure Safety Training Institute by coordinating
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Texas A&M Engineering Extension Service TEEX | Brownsville, Texas | United States | about 5 hours ago
to online learning platforms. Educational reimbursement assistance and release time. Leadership development programs TEEX Employer Notices Military Crosswalk Info Applications must either have
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, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical contexts. Our lab will be supported by two major grants starting in
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opening for a Patient Services Representative to support patient care and student learning. The ideal candidate is enthusiastic and committed to delivering stellar customer service. This talented individual
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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision