142 parallel-and-distributed-computing-"Meta"-"Meta" positions at Technical University of Denmark
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computer science and control systems architecture, advancing all the above disciplines. Building energy flexibility is an important resource for balancing and load shifting in energy networks, especially
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microstructural engineering will focus on in-situ optimization of the local distribution of grain sizes, crystallographic orientations, and stresses by the novel possibility of varying the laser beam shape and spot
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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computer science and control systems architecture, advancing all the above disciplines. Building energy flexibility is an important resource for balancing and load shifting in energy networks, especially
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. These are essential components for optical quantum computers and quantum networks, where one bit of information is encoded in the quantum state of a single photon. You will be part of a team of 10-12 people between
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airborne geophysical observations, and strong competences in Arctic fieldwork logistics. The gravity research group has carried out airborne gravimetry since the 1990s and has an annual work programme to
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emphasis on impacts of climate and environmental factors on fish early life history, growth, reproduction, survival and distribution including invasive species. You will closely work with colleagues in
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group focusing on the development and application of advanced proteomics, multi-omics, and computational strategies to explore cellular heterogeneity across both healthy and diseased stem-cell hierarchies
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academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon