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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | about 5 hours ago
Doctoral Candidate in computational neuroscience. (m/f/d) The position The position is part of an international collaborative effort (4 labs) to test computational models of episodic and spatial memory by
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will closely cooperate with the PhD student at GFZ in order to link the interpretation of geodetic GNSS measurements with the modelling of glacial-isostatic adjustment (GIA). You will focus your work
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results from numerical modelling of surface mass balance, firn compaction and ice flow dynamics identifying and quantifying processes of ice sheet change and ice mass balances developing stochastic
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integration of energy systems data and models and apply data science methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
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developing and using dedicated tools and processors Contribute to our sparse auto-differentiation libraries to accelerate the training of state-space models Collaborate closely with our internal partners
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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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, spectroscopy, synthetic organic and polymeric synthesis, biophysical and bio-chemistry, theoretical chemistry and mathematics , to advance the understanding of the emergence of complexity in molecular systems
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Description The Chair of Applied Mathematics at the Faculty of Mathematics and Geography at the KU Eichstätt-Ingolstadt invites applications for a position as Doctoral candidate in Applied
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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a