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to the supervision of PhD and/or Master’s students. Foster and sustain meaningful collaborations across local and national networks, including academic disciplines, professional sectors, relevant industries, and the
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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knowledge to real-world circumstances. Candidates must have a PhD, J.D. or other terminal degree in their academic discipline to be able to teach a graduate-level course. Candidates with an MA plus 20 years
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functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in Machine Learning for Cognitive
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of artificial intelligence, including statistical/machine learning theory, optimization, and deep learning. Successful candidates must have a strong record of research and teaching and should complement
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. Broward College places special importance on helping under - prepared students achieve academic success. Candidates should demonstrate (1) a mastery of their specific discipline; (2) a deep commitment to
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achieve academic success. Candidates should demonstrate (1) a mastery of their specific discipline; (2) a deep commitment to the college mission of fostering student success, achievement and persistence; (3
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reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and
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analysis (FEA) and strong skills in FEA software such as ABAQUS Hands-on experience in the construction and application of deep learning neural networks on material design by using PyTorch or Matlab PLEASE
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processing Graph signal processing Machine learning - supervised, unsupervised and reinforcement and tools such as TensorFlow, PyTorch, Keras and GreyCat Neuromorphic computing, spiking neural networks Deep