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General Summary of the Position Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display/20062865 ). The Zhou Lab at UMass Chan Medical
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | 2 months ago
expose the successful candidate to cutting-edge genome editor engineering approaches and the delivery of these reagents in vivo via AAV or lipid nanoparticles. The successful candidate will also learn
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive
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, you will work on a cutting-edge, multidisciplinary research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural
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. Strong research background in biometric security, presentation attack detection, or digital identity systems. Demonstrated programming skills in Python, MATLAB, or C++, and experience with deep learning
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep
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science, or public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced