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Field
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discovery. This position in particular focuses on sequence-to-function deep genomics modeling, with the goal of developing performant models that make generalizable out-of-distribution predictions
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have a PhD in Computer Science (or be able to demonstrate equivalent research experience in modelling and simulation, software engineering research) and possess a deep and demonstrable knowledge
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration, integrity, diversity and
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research interests in one or more of the following subfields: scientific machine learning, optimization, deep learning, uncertainty quantification, (Bayesian) inverse problems, reduced order modeling, high
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continuous learning and improvement. The successful candidate will demonstrate a deep interest in human security and governance research, a commitment to rigorous analysis, and a passion for contributing to
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. The responsibilities include modeling continual learning in the visual cognition domain at the interface of Deep Learning and Neuroscience. This position is contingent upon satisfactory performance of responsibilities
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learning and deep learning frameworks (e.g., Keras, TensorFlow, PyTorch) and statistical modeling Demonstrated ability to work independently and as part of a multidisciplinary team Excellent written and
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to time by the Project Lead. We hope you will bring: Experience of undertaking research in developing and deploying ML models (including regression, classification, deep learning, multi-agent
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning