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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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, Biostatistics, Statistics, Bioinformatics, Computational Biology, or other AI-related disciplines. Strong foundation in AI, statistical modeling, machine learning, or high-dimensional data analysis. Proficiency
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accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
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and UAV/drone-based environmental monitoring, as well as applications of machine learning. Experience in initiating, applying for, and participating in research and development projects, including
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UiO/Anders Lien 8th March 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat
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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: 2026/01/02 04
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this by concentrating on five select research areas in ICT. Learn more about: working at Simula and careers at Simula Project/Job description In the Department of ComplexSE, we are now offering a
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Genomics, Transcriptomics, and Bioinformatics Department Bashashati Laboratory | School
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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decision-making, and join a vibrant AI research community at the University of Texas at Austin, become members of The University of Texas at Austin’s Machine Learning Laboratory (https://ml.utexas.edu