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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-like AI models which can predict DNA structures Performing experiments for validation Participation in conferences in Germany and abroad (incl. presenting your research results) Preparing scientific
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremen, Bremen | Germany | 27 days ago
enabler of machine learning for eDNA-based assessments of deep-sea ecosystems” (m/f/d) Background Deep-sea ecosystems host highly diverse biological communities that provide key ecosystem functions and
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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training data Building transformer-like AI models which can predict DNA structures Performing experiments for validation Participation in conferences in Germany and abroad (including presenting your research
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools