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/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game theory and theory of metric spaces (with
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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assimilation, machine learning, and optimization techniques. Experience in student mentoring. Publications in leading journals within the field. Preferred Qualifications PhD in Environmental Modeling. More than
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in computational methods, particularly those with backgrounds in machine learning, artificial intelligence (AI), statistics, bioinformatics, or analysis-acceleration technologies. Candidates should
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences
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application and machine learning. • Author of at least 2 research papers in topics related to the project. • Knowledge on advanced control systems and optimization (please attach a description of your
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Squamous Cell Carcinoma Therapeutic Targets Using Systems-Level Machine Learning Based On Single Cell RNA Sequencing", implemented under Financing Agreement No. 760066/23.05.2023 The “Iuliu Haţieganu
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THE PROJECT PNRR" Lung Squamous Cell Carcinoma Therapeutic Targets Using Systems-Level Machine Learning Based On Single Cell RNA Sequencing", implemented under Financing Agreement No. 760066/23.05.2023
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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/admittance, force control Experience with Artificial Intelligence and deep learning concepts for robotics computer vision, tactile sensing, reinforcement learning Experience with robotic simulation tools e.g