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in the Nurse Anesthesiology Programs(see website: http://www.barry.edu/about/mission/ Teach anesthesia principles and basic sciences in didactic, simulation, and clinical settings and advise students
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological
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preference over external candidates. For questions about the vacancy, you can contact: Claire Stevenson, c.e.stevenson@uva.nl . Where to apply Website https://www.academictransfer.com/en/jobs/357583/phd
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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and supervising in bachelor and master programs Where to apply Website https://www.academictransfer.com/en/jobs/357657/phd-developing-reconstructions-… Requirements Specific Requirements We are looking
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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implement new nonlinear iterative solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned