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interdisciplinary approaches and methods to study biomolecular condensates. The intrinsically interdisciplinary nature of this field poses particular challenges—especially in establishing a shared scientific data
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on performance and project needs. **Qualifications** • For Doctoral Candidates: Master’s degree in Computer Science, Information Systems, or Mathematics. **Applicants must demonstrate:** • An excellent academic
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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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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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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 official DAAD template [doc-Datei] and ask your professors to email the confidential document to the GSPoL (admissions.gspol at uni-muenster.de ). Step 2: personal interview at the GSPoL Step 3
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with shallow water equations). Python coding for workflow control, data pre- and post-processing as well as model calibration and validation. High-performance computing (HPC) for running test cases and
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within the Institute of Theoretical Computer Science at TU Dresden. The main research area is the design and analysis of algorithms and data structures, with possible focus areas including randomized
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17Zipcode37077CityGöttingen Contact details Tel:+49 551 5176-100 E-Mail: golestanian-office at ds.mpg.de Web: https://www.ds.mpg.de/lmp Legal notice: The information on this website is provided to the DAAD by third parties