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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
, Carolina is an ideal place to teach, work and learn. One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional
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of Alternative Splicing for RNA Therapeutics Innovation”. The EpiTheraRx project aims to develop computational approaches for identifying disease-relevant alternative splicing events and their epitranscriptomic
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
(or surrogate models) are approximations of classical numerical solvers with a very low computational cost. They form the core of a digital twin. Using machine learning techniques to build these meta-models
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, cryo-electron microscopy (cryo-EM), cryo-EM-based polyclonal serology (cryo-EMPEM), molecular dynamics simulations, machine learning, and structural biology to define epitopes and engineer improved
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enrolled in ULB’s 4-year doctoral programme, in which they acquire many other important career skills. PROFILES Prof. Maes is looking for three highly motivated, intellectually curious, and collaborative
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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
DFKI, with a specific focus on the data, alignment, and representation-learning foundations required for robust and generalizable sign-to-text translation. Motivation and context Sign languages
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utility to more complex-shaped multi-layered structures is limited. In this project you will design and construct advanced DH systems to visualize complex optical samples comprising of multiple layers
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research and innovation centre for nanoelectronics and digital technology. The worldwide team of 6,000 scientists, engineers, and innovators from over 100 countries, is driven by a shared passion to push
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and