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Centre (NCN). The Principal Investigator is Dr. Eng. Piotr Kopka, email: Piotr.Kopka@ncbj.gov.pl Project description: The project aims to develop a new class of inverse Bayesian models called STE-EU-SCALE
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
00061514 Vacancy ID P020571 Full-time/Part-time Permanent/Time-Limited Full-Time Permanent If time-limited, estimated duration of appointment Hours per week 40 Work Schedule Monday – Friday, 8:00 am – 5:00
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
00061514 Vacancy ID P020571 Full-time/Part-time Permanent/Time-Limited Full-Time Permanent If time-limited, estimated duration of appointment Hours per week 40 Work Schedule Monday – Friday, 8:00 am – 5:00
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | about 2 months ago
are available, from computer graphics, computer engineering, computational physics, biology and chemistry, and so on. When training data is produced from simulation codes, it can be generated along with
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principles and analytic methods relevant in health services research Advanced knowledge of statistical computing and/or Bayesian inference Advanced programming skills in a common statistical software package
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QUANTITATIVE METHODS and is part of a cluster hire across the School of Social Sciences. The specialty area should be in human factors/human-computer interaction (HF/HCI), industrial-organizational psychology
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from, collaborate with, support, or improve humans; Deep Learning for Perception: Use of deep learning algorithms for computer vision, image and audio processing, and models of perception. The focus is
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for the eight-year project, developing software and maintain hardware such as computer, storage systems and scientific equipment for the collection and compilation, analysis, version control and publication
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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experimental methods. Develop and apply methods for demultiplexing, normalization/QC, effect-size estimation, biological inference, and predictive modeling. Contribute to biological manuscripts and methods