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of models like CNN, RNN, Transformers with some work in classical machine learning with XGBDTs is expected. Relevant work can lead to co-author publications and contributions to grant proposals. Tentative
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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North America, to improve an existing model for the spread of Cyvirus cyprinidallo3 (also known as Cyprinid Herpes Virus or CyHV-3) as a biocontrol agent for common carp in Australia. The modelling will
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Simulation – Data Analytics and Machine Learning (IAS-8) at Forschungszentrum Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired
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-learning–based segmentation, classification and tracking for microbes and microgels in phase-contrast and fluorescence images Optimise these models and pipelines for real-time performance and integrate them
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Is the Job related to staff position within a Research Infrastructure? No Offer Description We are seeking an ambitious candidate to develop Machine Learning models and frameworks for time series
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and unsupervised machine learning models, including LLM-based classification and fine-tuning for domain-specific applications. Collaborate with faculty and research staff on data collection and analysis
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software Solid foundation in machine learning and statistical modeling Excellent communication skills and ability to work in interdisciplinary teams We are also looking for the following competencies
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stage. 3. Preferential Factors Proven experience with decision support systems based on knowledge bases and machine learning. Previous experience in machine learning applied to dynamic systems or orbital
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
. Position Summary The position focuses on developing and applying advanced machine learning techniques to improve full-waveform inversion (FWI) across a range of imaging domains, including geophysics and