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. deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote
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%) Qualifications Required Qualifications: •PhD in biomedical engineering, psychiatry, neuroscience, physiology •Strong publication history and evidence of skill in MRI data acquisition and analysis •Ability
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the employee and the PI (15%). Qualifications Required qualifications A PhD in isotope geochemistry is required At least 5 years of hands-on experience in sample preparation in trace-metal-free clean labs
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/Qualifications Strong research track record in AI and scientific applications. Excellent knowledge of Machine Learning and Deep Learning. Strong Python programming skills. Experience with PyTorch, TensorFlow
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Qualifications Experience with machine learning and deep learning Ability to analyze and interpret complex geophysical data and apply appropriate research methodologies Additional Information: The College of Arts
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 3 months ago
research on population coding and Alzheimer’s disease Key research methods: fMRI (including 7T), MEG, virtual reality (VR), a wide variety of cognitive tasks, psychophysics, machine learning, deep neural
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network inference and modeling, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years
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. You are expected to bring deep mechanistic insight, drive the scientific narrative, and leverage our robust pre-clinical modelsâ”complemented by our clinical trial multi-omic dataâ”to build your own
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models