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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning
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microscopy, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand
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LanguagesFRENCHLevelBasic Research FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria •PhD in accelerator physics or a related discipline •Training or experience in accelerator physics
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emphasizes statistical inference within high-dimensional testing frameworks and incorporates robust feature selection into artificial intelligence techniques. The team collaborates closely with the Stanford
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, spanning fields such as neuroscience and cognition, humanoid technologies and robotics, artificial intelligence, nanotechnology, and material sciences, offering a truly interdisciplinary scientific
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the project “Centre for Credible AI at the Warsaw University of Technology”, financed through the FENG MAB programme (contract no. FENG.02.01-IP.05-0058/24). Candidates are required: 1) a PhD in Math, Computer
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Specification Essential Criteria: 1. Qualifications A good first degree in Computer Science, Artificial Intelligence, Data Science or a closely related discipline. A PhD in Explainable Artificial Intelligence
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data for European and international funding applications. Specific Requirements PhD in Physics, Engineering, Mathematics, Computing, Bioinformatics or related fields. Master's degree in Data Science
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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
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. The University of São Paulo's School of Arts, Sciences and Humanities (EACH-USP) is CIATec's host institution. Requirements: PhD completed less than 7 years ago in Education or related areas; experience in