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research project investigating self-supervised visual learning inspired by human perception. Responsibilities may include developing and evaluating machine learning and computer vision algorithms
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and the Faculty of Physics. We are seeking a highly qualified researcher with a strong background in tensor network methods, in particular the development of numerical tensor network algorithms, and
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novel algorithms, aiming for theoretical guarantees when working with structured and unstructured data. Pursue and complete a PhD thesis within the appointed 4-year duration. Join the Vienna Doctoral
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to changes. Knowledge, Skills and Abilities • Understands common data structures and algorithms. • Proficient in at least one programming language • Understands software architectures and deployment platforms
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evaluation of machine learning, computer vision, and other algorithms, primarily in the context of health. They will be part of the thriving research community of Duke Spark (spark.duke.edu) where AI
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, algorithms, applications, compilers, runtimes, and system-level constraints. Work with project teams to formulate and evaluate novel research ideas, develop research software and prototypes, mentor students
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: algorithm design for the interpretation of measurements, designing algorithms for deciding which experiments to perform, communicating with the instruments, orchestrating the steps of the research campaign
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assessment, and hydrographic research. This role translates research algorithms into reliable, documented, and reproducible production software; collaborates with scientists, engineers, students, and external
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efficient navigation algorithms for multirotor operations around vessels. The role will focus on integrating drone airspace-usage predictions with Vessel Protected Volume models, developing optimisation and
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algorithms (e.g., NPE) for strong gravitational lensing parameter estimation. Implement domain adaptation techniques to improve model robustness and transferability between simulated and real survey data