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: Research experience on natural language processing, causal inference. Research experience on medical domain. Knowledge of AI trustworthiness-related topics (e.g., fairness or explainable). Knowledge on AI
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
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experimental approach to systematically infer protein-semiconductor hierarchies materials and design de novo hierarchical architectures with atomic precision. The postdoctoral project aims to design de novo
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, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by
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approach to systematically infer protein-semiconductor hierarchies materials and design de novo hierarchical architectures with atomic precision. The postdoctoral project aims to design de novo proteins
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of the Saragovi Lab is to develop and apply a combined computational, Artificial Intelligence (AI) and high throughput experimental approach to systematically infer protein-semiconductor hierarchies materials and
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an applied research track in circular economy connected to sustainability measurement, financing, and procurement in the green transition. You will work in close collaboration with leading partners in
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. Two tracks to align with your goals: Choose between the academic or the entrepreneurial path. Global Collaboration: Opportunities for secondments at 29 renowned research institutions, infrastructures
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, Cellular or Molecular Biology, Biotechnology or similar Additional requirements are: - A strong track record in a relevant field of research, documented by first authorships in peer-reviewed original