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engineeringEducation LevelPhD or equivalent Skills/Qualifications We are seeking a scientist with: Expertise in image-based biological tissue modeling and simulation Good command of deep learning Expertise in coding and
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 2 months ago
of the ERC Starting Grant DYNASTY (Dynamics-Aware Theory of Deep Learning). The position might include traveling to conferences for paper presentation. Travel expenses will be covered within the limits
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. Interest in and knowledge of Georgia's Entertainment Industry preferred. Deep knowledge of workforce training for Georgia's film and television industry preferred. The University System of Georgia is
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, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
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mechanisms available in multi-parametric MRI, we aim to establish deep learning models that predict biomarkers of diseases progression and response to therapies, with applications in brain tumours and neuro
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, learn and grow. Join us and contribute to our culture. Additional Information: Purdue’s benefits summary https://www.purdue.edu/hr/Benefits/ Purdue will not sponsor employment authorization
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, 3-D dosimetry, oncologic and biological imaging, automatic treatment planning, radiomics and deep-learning, modeling of radiation damage for normal tissues and of tumor control using radiation
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an environment of care that nurtures the whole person-mind, body, and spirit. Here, you will find a deep sense of belonging, a culture that prioritizes well-being, and the opportunity to grow your career while
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connection with the legal adoption of an eligible child, such as travel or court fees, for up to two adoptions in your household. To learn more, please visit: https://www.hr.upenn.edu/PennHR/benefits-pay
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within the framework of project FLUIDNET: A Novel Deep Learning Architecture for Solving Viscoelastic Fluid Flows with reference 2024.13980.PEX, financed by national funds through FCT/MCTES, under