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Field
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trustworthy medical AI? Deep models already outperform humans on many benchmarks, yet in the clinic they remain black boxes: radiologists cannot see why an algorithm flags a lesion, and AI engineers cannot tell
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funding availability. Responsibilities • Design and implement deep learning architectures, AI agent pipelines, and computer vision algorithms to achieve project goals (33%) • Conduct full stack development
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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Requisition Id 15253 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative uncertainty quantification and visualization algorithms that enable trusted
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- conducting processors with respect to practical short-depth (NISQ) quantum algorithms Cooperate and actively work with experimental partners developing quantum processors using these technological platforms
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Notebook; apply foundational knowledge of ML algorithms. Work closely with and provide guidance to interns and part-time student developers. Job Requirements: Preferably bachelor’s degree in Computer
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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employment This is a 7-month position from October 1st, 2025, or as soon as possible. Job description You will participate in the ongoing development of an algorithm platform for digital shadows and twins
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surveys, data-intensive algorithms, and software. The Institute comprises seven faculty and senior fellows, and over 30 postdoctoral researchers, research scientists, software engineers, and graduate
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magnetometry measurement and analysis of the Nab spectrometer magnetic fields, help develop the BL3 DAQ and algorithms, carry out Monte Carlo simulations for Nab and BL3, and help undergraduates at EKU finish