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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
behaviors is particularly challenging due to inherent path dependence and the evolution of unobservable internal state variables. The objective of this PhD is to propose novel hybrid modeling architectures
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Research Scientist IV, Information Science (Extended Temporary) (Remote Work Available) Posting Number req25414 Department Information Science Department Website Link https://infosci.arizona.edu/about
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environment, and climate change. Requirements A PhD in Wind/Civil/Structural Engineering or a closely related field (e.g, Environmental Engineering, Mechanical Engineering, and Aeronautical
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Systems (S3D) is one of the seven academic departments of the Carnegie Mellon School of Computer Science (SCS). S3D hosts the SCS PhD programs in Software Engineering (SE) and Societal Computing (SC), along
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new member for our working group “Process Engineering for Fiber Plants” in the department “System Process Engineering”, starting on April 1, 2026. Scientist (for PhD) (m/f/d) In the DFG-SFB, coordinated
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are programmed. This includes defining novel programming methods and compiler infrastructures to deploy optimized software onto heterogeneous computing systems in both the embedded and high-performance computing
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of a vision transformer, U-Net architecture, or Diffusion model that you trained yourself. Projects in computer vision for microscopy image analysis are especially relevant. Include a link to a code
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success. Work with technical architects on the team to validate design and implementation approach. Take ownership of architecture design and development of scalable and distributed software systems. Own
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architectures. This includes among other: (a) design and implementation of machine learning and GenAI models, (b) efficient training and inference on GPU-based systems, (c) fine-tuning and optimization of large