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candidates from all backgrounds to join our community. The School of Electrical Engineering is one of the six schools of Aalto University. Our portfolio covers fields from natural sciences to engineering and
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collaborations. Applicants must hold a Ph.D. in science, engineering, or a closely related field awarded before the start date, and is within the first five years post-receiving a PhD. Competitive candidates will
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undergraduate students to contribute research, publication and presentation. Minimum Qualifications: PhD in data science, computer science, materials science, electrical engineering or related field from
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. You hold courses independently as defined by the collective agreement. You (co-)supervise bachelor, master, and PhD students, and you independently propose bachelor, master, and PhD projects to this end
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and Experience The candidate must have a PhD degree in Microbiology, Genetics, Molecular Biology, or a related field by the position’s start date. Experience in microbial genetics and cultivation
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refereed journals, the fellow will develop lay materials to transfer key findings to farmers and agribusiness. Qualifications Minimum Qualification: PhD, by time of appointment. Candidates need to have a PhD
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 14 days ago
is based on optically detected magnetic resonance (ODMR) or electrically detected magnetic resonance (EDMR), utilizing the spin properties of quantum centers/defects in large band gap materials like
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asymmetries along the former Inner-German Border” was launched in October 2024 (https://divsky.hypotheses.org/ ). It brings together around ten researchers and lecturer-researchers (UMR Printemps, UMR
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commitment to integrating AI in their scholarship and instruction and may come from any discipline. A PhD is expected prior to the start date. The Postdoctoral Scholar will serve as one of the Initiative’s AI
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Do you want to contribute to the future of AI-driven electric transport systems? Join our research group to develop advanced machine learning methods for electromobility, focusing on energy-aware