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Field
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machine learning (ML) algorithms to identify previously unknown correlations between synthesis parameters (inputs) and optical, electronic and chemical properties (outputs), such as quantum yield, light
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deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research of integration of multimodality data with clinical, radiological images
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
developing novel methods and algorithms for applications in the Engineering Design domain; demonstrating a commitment to deliver results; working on research proposals; and working in a team environment. Job
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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pipelines and algorithms to construct and evaluate foundation models for whole-body and abdominal MRI. Alongside this, you will conduct comprehensive and systematic literature and database searches related
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The researcher will use and further develop ptychography algorithms for transmission electron microscopy data
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Inria, the French national research institute for the digital sciences | Bron, Rhone Alpes | France | 18 days ago
. Test model predictions in behavioral experiments. Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems
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learning, particularly in deep learning or related areas. No prior knowledge of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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approaches, such as topology optimization, with gradient-free methods, such as genetic algorithm, in order to exploit the specific strengths of the two classes of algorithms, and through this reach an ideal