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) , Lattice QCD , Machine Learning , Neutrino Astronomy , Neutrino physics , Nuclear and Many-Body Theory , Nuclear Theory (nucl-th) , Particle Astrophysics , Quantum Field Theory Appl Deadline: 2026/01/01 11
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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, inverse problem, machine learning Expertise in the analysis of a wide range of geospatial and geodetic data sets Strong expertise in programming with Python or MATLAB As position requires stakeholder
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within the role to pursue an independent research project in the general remit of gene expression and the lab. Candidates with interest or experience in machine learning, artificial intelligence and
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the following mandatory requirements: a) A completed degree in Chemical and Biological Engineering; b) Good knowledge in the areas of Machine Learning, Microbiology, Knowledge Graphs, and Language
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at Trinity College Dublin (TCD) seeks to appoint an outstanding, enthusiastic, and highly motivated Research Fellow who will contribute to a project that uses machine-learning methods to forecast
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researchers on 5G testbed operation, integration and performance testing. Job Requirements Bachelor’s, master's or Ph.D. in Electrical and Computer Engineering, Computer Science or related field. Solid
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability