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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
/inverse methods expert within SERENE. Assist in the training and support of other early-career members of SERENE. Main Duties The responsibilities may include some but not all of the responsibilities
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external fundings when appropriate. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team
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Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written communication skills Proven
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for building Data+AI systems. Writing research papers of high quality based on research results Building deployable Data+AI systems based on the research results. Job Requirements: Preferably PhD in Computer
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grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. Key Responsibilities: Literature review of existing methods and
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academic institutions to translate research into viable solutions. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly
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conventional simulators. Finite element-based methods such as Mixed-Finite-Element or Control-Volume methods are convenient thanks to their suitability for complex unstructured grids. Applications are sought
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Details Title Postdoctoral Fellowship in Electrical and Computer Engineering School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position
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Senior Research Fellow (Human Factors / Human-Computer Interaction / Engineering / Computer Science)
Human Factors, Human-Computer Interaction, Engineering, Computer Science, or a related discipline. Experience in Human-AI Teaming, Human-Automation Interaction, or Human-Centred AI, preferably in safety
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and