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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/12 11:59PM ** (posted 2025/10/21, listed until 2026/04/21) Position Description: Apply *** the listing date
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Grade Level 06 Salary Range $18.20-24.82/hour Type of Position Staff Position Time Status Full-Time Required Education HS Click here for more information about equivalencies: https://hr.uky.edu/employment
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Lexington, KY Grade Level 06 Salary Range $18.20-24.82/hour Type of Position Staff Position Time Status Full-Time Required Education HS Click here for more information about equivalencies: https://hr.uky.edu
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Lexington, KY Grade Level 44 Salary Range $43,805-70,075/year Type of Position Staff Position Time Status Full-Time Required Education MA Click here for more information about equivalencies: https
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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guided by mana whenua, Ngāi Tūāhuriri, and valuing te reo Māori. Excellent computer literacy skills, with proven ability to learn and grow knowledge for relevant software and technology. Excellent oral and
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significant modeling challenge. This project addresses that challenge by combining machine learning with constitutive modeling, while ensuring adherence to physical laws. Although the primary focus is on large
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 6 days ago
verification, theory of computation, formal methods, human-computer interaction. Intelligent Systems: artificial intelligence, machine learning, computer vision, natural language processing, robotics Data
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for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration