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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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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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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
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Golestanian Address StreetAm Faßberg 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
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physics, to experimental biophysics and biochemistry, and, to cell and molecular biology involving data science. A position comprises 65-75 % of the fulltime weekly hours and is limited until March 31, 2030