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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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) Turtle Neuroanatomy & Evolutionary Morphology (part time, 65%, 26 weekly working hours) We are seeking a highly qualified and motivated Research Assistant (f/m/d) to contribute to a research project
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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motivation in conducting evolutionary and chemical ecological research at different levels. •MSc/diploma in a relevant field (e.g., evolutionary biology, chemical ecology, sensory biology). •Strong experience
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tasks: Computationally design and simulate neuromorphic hardware including novel materials, devices and circuits. Implement bio-inspired learning algorithms on said hardware. Collaborate with
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Palaeoenvironment (SHEP) at the University of Tübingen is seeking a motivated PhD candidate for the Paleontology working group: PhD Candidate / Research Assistant (f/m/d)Turtle Neuroanatomy & Evolutionary Morphology
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we