608 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" PhD research jobs
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behavioral well-being, particularly among vulnerable populations. Applicants are encouraged to approach these topics from perspectives such as human-machine communication, mobile communication, and health
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Website https://www.jobbnorge.no/en/available-jobs/job/292474/researcher-at-the-departm… Requirements Research FieldBiological sciences » BiologyEducation LevelPhD or equivalent Research FieldBiological
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Grenoble Observatory of Universe Sciences (OSUG) and a joint research unit (CNRS, UGA) with approximately 170 staff members and numerous temporary employees. https://ipag.osug.fr/ The CHARM team, led by
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LIP - Laboratório de Instrumentação e Física Experimental de Partículas | Portugal | about 2 months ago
methodologies, including the manipulation and processing of satellite imagery and data from equivalent remote-sensing platforms (e.g., UAVs), as well as the development of machine-learning approaches to enhance
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the Ph.D. candidate is enrolled as a Ph.D. student at Aarhus University Graduate School of Health ( http://phd.health.au.dk/ ) in a separate procedure before starting as a Ph.D. student. The successful
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, of January 25th. The presentation of such Recognition is mandatory for contract signature. More information can be obtained in: https://www.dges.gov.pt/en/pagina/degree-and-diploma-recognition . II
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Selection process https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025/1985/anunt.pdf https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025/1985/tematica.pdf Additional
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Selection process https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025/1986/anunt.pdf https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025/1986/tematica.pdf Additional
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- 4 Additional Information Selection process https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025/1987/anunt.pdf https://www.ubbcluj.ro/ro/infoubb/posturi_vacante/files/proiecte/2025
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focuses on advanced methodologies in abdominal imaging, particularly applications of machine learning and deep learning to medical image analysis. The lab aims to advance existing imaging techniques and