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Field
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are proficiency in Python, deep learning frameworks such as Keras, PyTorch, TensorFlow, or other related software stacks, and a solid background in machine learning, cybersecurity, or AI robotics, and the ability
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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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field Preferred Experience Deep learning and advanced AI expertise. Simulation and digital twin modeling. Demonstrated research mentorship and team leadership for undergraduate and graduate students
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closely related field. Required Skills : Proficiency in Python/C and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or Keras). Interest in or prior experience with
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/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
methods for factuality/hallucination testing, robustness, calibration, bias/fairness, and/or adversarial stress-testing. - Solid programming and ML/NLP engineering skills in Python and ideally modern deep
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
, robustness, calibration, bias/fairness, and/or adversarial stress-testing. - Solid programming and ML/NLP engineering skills in Python and ideally modern deep-learning stacks (e.g., PyTorch/JAX, HuggingFace
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of the appointment. Demonstrated experience with deep learning methods or sophisticated mathematical frameworks applied to large-scale or scientific datasets. Experience working with observational seismology data (e.g
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engineering, data science, statistics, mathematics, physics or an adjacent subject, with focus on medical image analysis and/or deep learning. Furthermore, the following competences will be expected
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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying