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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML
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. Joerg Hoffmann at University of Saarland. 2) 1-2 PhD students working with Prof. Hendrik Blockeel and/or Prof. Jesse Davis on the topic of developing novel approaches for learning, compressing, and
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systems characterized by a high spatial and temporal variability of energy supply and demand. We look forward to your application. We are offering an interesting PhD position – Learning Tailored Iterative
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AI / Machine learning / Computational Oncology lab:Our work is translationally focused, towards realizing our vision of developing new approaches for fast and low-cost prediction of patient response
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the neurovascular space. Knowledge of neurovascular anatomy, acute stroke, endovascular treatments, neuroendovascular devices for the treatment of stroke. Ability to generate machine learning analysis of medical
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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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practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in neuroscience, machine learning, and clinical informatics
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affected by warping, addressing both audio analysis and synthesis tasks. The methodological scope spans stochastic signal processing and machine learning, including hybrid physics‑guided and data‑driven
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position as Senior Lecturer. Optimization, machine learning, and control theory together form a central toolbox for understanding, analyzing, and controlling complex systems. These fields span deep
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the