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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 8 days ago
transmembrane cargo proteins and drive the formation of transport carriers within the endosomal network. In this mission, we primarily rely on cryo-ET and subtomogram averaging to visualize molecular machines in
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drive the formation of transport carriers within the endosomal network. In this mission, we primarily rely on cryo-ET and subtomogram averaging to visualize molecular machines in reconstituted form
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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) under the supervision of Dr. Elina Spyrou . Summary of Project: Power systems are at the core of the transition to net-zero energy systems, and they have to transform in two ways. First, their generation
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research position in a dynamic and collaborative environment Extensive opportunities for personal development and training, e.g. through an extensive range of training courses; a structured program of
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PhD: Recognizing Multicultural Strengths of Youth via Social Networks at Work Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application
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to the new theoretical development of graph deep learning, and contribute to the research community of brain connectivity and network neuroscience. This is a joint Phd studentship between Coventry (UK) and
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for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature, 2024, 631, 549–555). This work demonstrated that self
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for: computers able to process information more like the brain, studying how to reproduce some of the properties of biological neurons and synapses using networks of molecules and nanostructures, and other
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system