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materials property predictions. A deep understanding of materials properties and close connections in academia and industry enable the group to explore exciting research avenues. For more information about
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experience in deep learning frameworks (TensorFlow/PyTorch) Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences Knowledge of phylogenetics, protein
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are comfortable navigating complex HPC environments and wrangling large datasets. You have experience with modelling through state-of-the-art machine and deep-learning methods and with hands
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artificial intelligence techniques: deep learning or swarm intelligence is a plus but is not required. The annual base salary range for this position is $85,000 - $100,000. When extending an offer
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associate will also provide leadership in coordinating different projects and advising more junior lab members. The current and prior work of the lab include deep learning algorithms for detection
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data from both tissue and single cells, for improved understanding of Alzheimer progression. Experience in brain disorders, machine learning and deep learning will be a plus. Interested candidates should
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Title: Postdoctoral Research Associate - Machine Learning & Advanced Manufacturing Employee Classification: Postdoctoral Research Assoc Campus: University of North Texas Division: UNT-Provost
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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moves. Success will be measured by having published or contributed to papers in top venues (e.g., Nature Science of Learning, Computers and Education, ACM Learning at Scale, Educational Data Mining) and
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored