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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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Free probability theory High-dimensional probability, concentration and functional inequalities Mathematical aspects of machine learning and deep neural networks Free Probability aspects of Quantum
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research
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Education and/or Work Experience M.D. or PhD Required Qualifications Ph.D. in any of the following areas: Neuroscience, Pharmacology, Physiology, or Biochemistry. Certificates/Credentials/Licenses Ph.D. in
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high intellectual merit and the willingness to use machine learning and/or AI techniques. Essential Duties and Responsibilities: Successful candidates are expected to develop an impactful research
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Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning, statistics, data science, applied math and/or other
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superconducting qubits and millikelvin electronics Did you recently get your PhD in circuit quantum electrodynamics (cQED) and are now looking into taking the full potential of your skills into use for making new