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Field
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frameworks for quantum machine learning, including conformal quantum prediction and uncertainty quantification in quantum models; Theoretical and algorithmic advances rooted in statistical learning theory
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are looking for a motivated postdoc with solid experience in bioinformatics and in machine learning. The position centers on SRPs and LLPS, and the successful candidate will contribute to several ongoing
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motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins
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, and other methods. Collects data using cameras, sensors, and data acquisition systems. Prototypes, troubleshoots, and fine-tunes machine learning models for outcome predictions relevant to surgery
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap-specific signals from raw
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for individuals preparing for academic careers in statistics, biostatistics, machine learning, or related fields. The postdoctoral researcher will be supervised by Dr. Razieh Nabi. For inquiries, please contact
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the LAMP group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is for 2-3 years and linked to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning