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Field
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of analytical data to guide the generation of highly accurate 3D/2D molecular graphs or SMILES representations. Research Aims and Objectives This project aims to develop a robust, data-efficient deep learning
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the traditional custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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of international publications. In addition, the following would be welcome: Practical experience with deep-learning frameworks (e.g. PyTorch, TensorFlow) and knowledge of modern architectures such as PINNs or graph
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publications. In addition, the following would be welcome: Practical experience with deep-learning frameworks (e.g. PyTorch, TensorFlow) and knowledge of working with Large Language Models (LLMs), Retrieval
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safe and non-discriminatory living, learning, and working environment for all members of the university community. The office consists of several teams: Civil Rights, Disability Equity Office, Intake and
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expertise (within the fields of marketing, innovation, or design). The appointment is slated to begin in August 2027. We seek a teacher-scholar to develop and teach courses that focus on digital technologies
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from the Finnish Meteorological Institute (FMI), as well as the DR will develop new and improved in-orbit E-sail experiments for future missions in lunar orbit and deep space. The DR will be enrolled
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: A doctorate in a Machine-Learning related field A deep knowledge of Control Theory, both classical and deep learning based A solid publication record in top level ML venues such as NeurIPs, ICML, and
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Visual Arts: Graphic Design at the rank of Assistant Professor to begin Fall 2027. We seek candidates who embrace USD’s teacher-scholar model: an active practitioner in the field of design with a deep