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scientists on developing noise-aware quantum programming paradigms Your Profile: Master in physics, computer science or comparable fields Good knowledge of quantum theory, ideally quantum information theory
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please visit the link provided in the section above. For inquiries, please contact the responsible PI. Contact and information will be provided on the offered position webpage. Selection process Eligible
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Your Job: At the Electrocatalysis department of Prof. Karl Mayrhofer, we offer a PhD position within the team Nanoanalysis of Electrochemical Processes. Lead by Dr. Andreas Hutzler, the team is
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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reimbursed. Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website
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The INM research group Structure Formation is seeking for a motivated Doctoral Researcher interested in the self-assembly of nanoplatelets (NPLs) for optical applications. This PhD project is part
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 3 months ago
breeding populations to investigate the genetic basis of traits such as visual anther extrusion, heading date, plant height, and grain yield. You process genotypic data, ensure its alignment with phenotypic
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic