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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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://drive.google.com/file/d/17SY2HfckkPZp8GVtRelkIxrGhikP92nN/view?usp=sharing Job Requirements REQUIRED: PhD in materials science, physics, electrical engineering, chemical engineering, or a related field by the start
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Project summary: How does exposure to electricity price risk shape the way investors value European companies? Using textual analysis and financial econometrics, this PhD will construct new
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INESC COIMBRA - INSTITUTO DE ENGENHARIA DE SISTEMAS E COMPUTADORES DE COIMBRA | Portugal | 5 days ago
support from FEDER and national funds through FCT, I.P., under the following conditions: Scientific Area: Energy Admission requirements: Holders of a PhD degree in Electrical and Computer Engineering, Civil
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to adjust across different tasks and domains. Preferred Qualifications Doctor of Philosophy (PhD) in scientific or engineering field, preferably in computer science, electrical engineering or ICT. Experience
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Basse Normandie. PHD title: Doctorat de Physique PHD Country: France Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/137565 Requirements Specific Requirements Applicant
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, Electrical and Computer Engineering, Cybersecurity, or any other relevant field. • Strong background in cybersecurity. • High proficiency in programming (C, C++, Python, ROS, or Matlab). • High proficiency in
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of intelligent and secure energy systems where artificial intelligence meets reliable power electronic systems? We are looking for two motivated PhD candidates, as part of the AI:X initiative at Aalborg University
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Engineering » Electrical engineering Engineering » Control engineering Physics Mathematics Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD