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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | about 3 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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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models
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, specifically battery prototypes. What you will do Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as
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networks in vitro that severe mental disorders, such as the model SNAREopathies are a diverse group of brain disorders caused by mutations in eight genes that together drive the secretion of chemical signals
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-frequency Joule losses. Litz wire is one of the most promising solutions due to its exceptional ability to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully
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of the Research School will be anchored in a robust thesis advisory committee (TAC) model, designed to provide comprehensive support and guidance to PhD students throughout their research journey. Each student will
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improve the efficiency, maneuverability, and noise performance of drones and other multirotor aircraft, but their deployment requires more advanced modeling and control methods. The PhD will focus
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and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis, modeling and simulation. The advertised position is partly funded by the German Research
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scanning probe instrumentation fabrication of van der Waals heterostructures transport, optical spectroscopy, and quantum sensing experiments data analysis, modeling, and scientific communication
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learning and AI models to imaging data [3]. The student will be tasked with the development of new models for the early detection of CIN cancers, applying bleeding edge computational methods and machine