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, or other related fields. Solid Mathematical skills. Experience in implementing algorithms for machine learning and natural language processing-related applications (It would be good if the candidate can also
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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distribution systems, EV charging modeling, distributed energy resources, optimization, control, machine learning, hardware-in-the-loop simulation. Expertise in programming languages such as Python, C
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual
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with machine learning techniques for robotic decision-making and intelligent control for tasks with high uncertainties. Experience with research on multi-agent collaboration and decentralized control
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for the development of professional career skills, on-going technical and workplace learning, and an opportunity to apply subject matter expertise to new technologies, digital scholarship, creative writing, etc. All
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balance of supervised investigation and work experience in a learning environment that will expose the participant to activities across the drug development process. We are seeking scientists from U.S
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. The research program embraces team science drawing from i.e., implementation science, data science, geospatial, epidemiological, and machine learning approaches to better understand broad contexts including
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University of Western Sydney | The University Of Sydney, New South Wales | Australia | about 2 months ago
of the University may be required. Please review the position description for further information. About You You hold a doctoral qualification in Cognitive Neuroscience, Machine Learning, Computer Science or another
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transcriptomic data, that will be integrated with clinical metadata and whole-genome data for developing machine learning models to identify and predict patient factors driving toxicity response and sensitivity