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of information theory, mathematical modeling and machine learning and their application to medical science problems (5) Deep Learning in Biomedical Sciences (6) Theory and methods on prediction, control and
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networks; experience in applying machine learning models and processing imagery from UAS and satellite platforms. Other requirements: Willingness to work irregular hours and in occasionally adverse weather
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training a machine learning algorithm on Greenland satellite – model surface melt differences and characterize what atmospheric forcings are most strongly associated with meltwater production biases using
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experience. Research background in decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow
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European Lab for Learning & Intelligent Systems (ML/AI). Cell engineering for cancer and immune diseases (Bock Lab ). Development of innovative technologies for biomedical research, for example building upon
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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researchers with an interest in any of the following fields: quantum gravity, field theory, machine learning, statistical physics, random matrix theory, or complex networks. The job description may be changed
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive difference to society and contribute to a sustainable
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, machine learning, and plant genomics. Our lab seeks to explore and understand the network of plant genes, their regulation in response to environmental stress at the single-cell level, and the conservation