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Field
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. You will support field behavioral experiments to test designs of tradable credits schemes in specific urban contexts. You will explore and implement machine-learning algorithms and classical dynamic
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will have the opportunity to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD
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construction, and a robust foundation in statistical spectral analysis, including familiarity with (or strong interest in) chemometrics and/or machine learning algorithms. Job requirements The Ideal Candidate
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machine learning, modeling algorithms, and/or mapping applications. Applicants must have a PhD in ecology, wildlife sciences or geospatial modeling. Additional Information: Salary Information: Commensurate
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verification algorithms and prototypes on large microgrids, Naval systems, and utility systems. ● Assist grant proposal writing, work collaboratively with industry and government, and mentor graduate
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algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a team • Proficiency in programming languages (e.g. Python, R, Fortran
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Eligibility criteria . PhD in atmospheric sciences • Knowledge of cloud physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work