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towards a future-proof logistics system with a special focus on machine learning-based collaborative scheduling, resource sharing, and self-organisation. The EngD position corresponds to a 2-year post
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, computer vision, machine learning, and/or related Programming and technical skills, including GitHub profile (if existing) List of two referees (including contact details) and (if available) their support
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robotics, and human-machine interfaces. We are looking for an ambitious researcher who is looking to use this opportunity to grow towards the next level of research. The position is renewable each year based
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-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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. As a Carnegie Doctoral/R2 institution, our world-class scholars instruct about 26,000 students in associate's, bachelor's, master's and doctoral level degree programs. Whether you are seeking the charm
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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that the candidate has prior research experience in one or more of the following research topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning
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, United States of America [map ] Subject Areas: Statistics, Machine Learning, and AI Appl Deadline: 2025/07/01 11:59PM (posted 2024/10/31, listed until 2025/07/01) Position Description: Position Description The University
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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning