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and develop skills in the growth of cultured tissue, an area of growing academic and industrial interest Opportunities to develop skills in biophysical models of tissues and deep learning techniques Key
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application of advanced deep learning models, with an emphasis on techniques such as knowledge distillation. The candidate will engage in research involving time-series analysis, including modeling, forecasting
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applying causal inference, spatial econometric modeling, and machine learning techniques to questions of regional economic development, urban sustainability, or entrepreneurship ecosystems. Proficiency in
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of innovation and tradition. Renowned for hands-on learning and pioneering research, Mines educates future leaders in STEM fields who will make a meaningful impact on the world. Our vibrant community
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of the Postdoctoral Research Associate includes contributing to multiple projects including resilience-aware scheduling, deep learning workload job scheduling, and storage system performance tuning. The candidate will
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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that combine situated ethnographic detail with deep knowledge of cultural and political contexts and histories, ideally in a cross-national comparative perspective. You can learn more about the research
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learning, or related quantitative field. • Proficiency with deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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computational biology and bioinformatics, with experience in: Applying deep-learning or AI-based methods for image denoising. analysis of whole-genome sequencing (WGS) or other large-scale sequencing data