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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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at obtaining further academic qualification (usually PhD). Research area: Systems of interacting particles are ubiquitous in natural and social sciences. Typically, they comprise many agents that, through intra
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(atmospheric entry flows) ability to work efficiently and self-reliantly in a diverse inter-disciplinary and multi-cultural environment ability to work in a team as well as independently ability to solve complex
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fields can be found on: https://www.isse.tu-clausthal.de/forschung/forschungsgruppen/dgt-digitized-green-tech Our excellent Network on international and national basis offers a perfect start for a
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interest in agentic AI, online learning and optimization, and applications in economics. The full-time positions (100%) are initially offered for two years, with the possibility of extension, depending
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ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en ) Eligibility requirements
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of the host organization of last 36 months. As secondments and events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu
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, tumor biology, and molecular biology. Our research aims to develop creative and innovative strategies to characterize new classes of molecular imaging agents and advance those with translational potential
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), Condensed Matter Physics (Fluid mechanics and dynamics) and Applied Physics (atmospheric entry flows) ability to work efficiently and self-reliantly in a diverse inter-disciplinary and multi-cultural
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multi-disciplinary research environment organized within a structured PhD program that enables to complete a doctoral thesis within three years. Topics: Researchers of several disciplines