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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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segmentation, target detection and change detection along and across multiannual series of data. Methodologies like foundational models, machine learning, deep learning, multitask learning, enforcement learning
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will design and validate advanced multi-agent Deep Reinforcement Learning (DRL) and/or Digital Twin (DT)-enabled methods for efficient, scalable and time-critical handover optimisation. The work will
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multisensor fusion and ondevice AI pipelines that guarantee tight latency, power efficiency, and fail-safe robustness. Driving hardware–software codesign to radically optimize state estimation and deep-learning
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basic experimental design. Hands-on experience with classical machine learning methods such as linear/logistic regression, decision trees, and gradient boosting. Familiarity with deep learning concepts
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monitoring agricultural emissions across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH3, N2O, CO2, CH4) using satellite
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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Macomb Community College Adjunct Faculty demonstrate deep subject matter knowledge and provide effective instruction to students using various modalities including, but not limited to, on campus, online
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | about 22 hours ago
signal and image processing, machine learning, deep learning, robotics, and 3D design, with proficiency in programming languages such as JavaScript, R, Python, Matlab, or SQL. • Proven research experience
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employment. Starting date: 09.04.2026 Job description:PhD Position: Deep learning for phase-contrast synchrotron X-ray tomography Reference code: 987 - 2026/WP 1 Work location: Hamburg Application deadline