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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
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, statistics, and machine learning. We offer a full-time position with the following benefits: Flexible working conditions including negotiable part time contract. Monthly gross salary: 54 - 72 thousand CZK
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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, automated reasoning and symbolic AI, and machine learning and neural AI. Applicants should submit their cover letter, research statement, CV, and list of references to positions@icarm.io. Applications will be
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++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents, or similar. Solid expertise in
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groundbreaking symbiosis of cutting-edge AI combined with human support. To learn more please visit https://www.kcl.ac.uk/research/embrace About the role The Research Fellow in Digital Health & Data Sciences is
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fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure