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-processing, and machine learning textual analysis of the full text of policy documents. Qualitative content thematic analysis is envisioned to compliment structural topic modelling to identify strategies and
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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The TUM School of Computation, Information and Technology at the Technical University of Munich (TUM) welcomes applications for a PhD or Postdoc Position (m/f/d, 100%, 2 years+) in Numerical Mathematics
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Your Job: In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect
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existing quantitative methods in international nuclear safeguards with state-of-the-art artificial intelligence/machine learning (AI/ML) algorithms Contribute to standardising methodologies for calculating
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service
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, survey) or applied microeconometrics, and applied economics. You have experience with big data and machine learning methods? This would be a particular asset! With excellent English language skills, both
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and