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at the interface of data science and the scientific domains pursued at the three participating Helmholtz centers. Methodologically, a broad range of topics is covered, from large-scale data management to data mining
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of Prof. Dr. Frank Cichos and Dr. Nico Scherf (Max Planck Institute for Human Cognition and Brain Sciences). The position is part of a collaborative project in the Center for Scalable Data Analytics and
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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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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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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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cells from different mouse models with accelerated aging phenotype. The work of the PhD candidate will include data mining and integration of these datasets with resulting identification of candidate
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). The Schirmeier lab is a small multicultural research group with PhD students and postdocs of different nationalities. Thus, the group’s communication is in English. We aim to analyze the metabolic homeostasis
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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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the egg are essential for early development, yet the molecular events that awaken these transcripts remain poorly understood. During my postdoc, I identified proteins and mRNAs localized to storage granules
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-ethnic environment with sensitivity and respect for diversity Desirable: Experience in cheminformatics, molecular modeling, or enzyme mechanisms Familiarity with databases, data mining, and knowledge