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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor
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(e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) Strong skills in machine learning and deep learning Experience
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leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence will enable the rapid evaluation
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. Being independent and able to interface with multiple work groups is critical. We use biochemical, proteomic, cell biology, molecular biology, genomic, epigenomic, and mouse model approaches and
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develop models that can read, extract and acquire knowledge from legacy data, coming both in the form of text and in the form of structured data (e.g. physical measurements) to predict characteristics
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. The objective of the research is to use machine learning methods to find models of ship trajectories and traffic patterns that can be used to detect anomalies and predict into the future. The basis for this is
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a strong background in computational modeling, optimization, and systems engineering, eager to develop advanced design and simulation frameworks for battery pack topology and e-powertrain integration
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of students and can include, technique development, microscopy-spectroscopy, analysis/programming (including AI and machine learning) and materials-focused studies. We use innovative high-resolutionidentical
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the PhD, you will gain expertise in finite element modelling, electronic control and instrumentation, machine learning, experimental methods, and advanced signal processing. You will also build strong
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applicants who have a background or strong interest in Computer Science, interactive media, software engineering, 3D modelling/animation, VR/AR, human–computer interaction or related digital-tech fields