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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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-friendly environment with multiple actions to attract, develop and retain women in science · 32 days’ paid annual leave, 11 public holidays, 13-month salary, statutory health insurance
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management of forests. We have a multidisciplinary profile, with global relevance and specialized expertise on forests and forestry as complex socio-ecological systems. We closely collaborate with multiple
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Research Assistant Professor, Research Associate Professor, Assistant Professor, Associate Professor
cardiovascular, neuro-, and cancer pharmacology and developmental and neurotoxicology. Faculty have multiple collaborations with scientists across the university and with academic, government, and industrial
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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Thebault labs are seeking a skilled and motivated Research Assistant to support data analysis and pipeline development for cutting-edge research in neuroinflammation, multiple sclerosis, and
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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executing data collection and analysis for multiple research projects, with special attention to Crisis Response projects. Candidate must have a degree – preferably a master’s degree, PhD, or PhD candidate
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executing data collection and analysis for multiple research projects, with special attention to Youth Justice projects. Candidate must have a degree – preferably a master’s degree, PhD, or PhD candidate
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high