1,706 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" Fellowship positions
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SAPIENZA UNIVERSITA' DI ROMA-DIPARTIMENTO DI PSICOLOGIA DINAMICA CLINICA E SALUTE | Italy | 29 days ago
parent–child relationship. Where to apply Website https://www.uniroma1.it Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows: Italy Eligibility of fellows
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the Ministerial Order nr. 33/2019 (January 25). The presentation of the recognition certificate is mandatory for contract signature. More information can be obtained at: https://www.dges.gov.pt/en
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research . We are looking for a Research Fellow to work on the catalysis for CO2 related
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in its excellent research capabilities in areas including automotive mechanical and electrical design, data management, life cycle sustainability management through technology and materials innovation
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Matcher (https://www.sheffield.ac.uk/eee/people/academic-staff/stephen-matcher ) and the Human Parturition Research Group of Prof Dilly Anumba (https://www.sheffield.ac.uk/smph/people/clinical-medicine
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or equivalent Additional Information Website for additional job details https://arquivo.ciencias.ulisboa.pt/servicosCake/servicoLogos/Concursos/verAnex… https://arquivo.ciencias.ulisboa.pt/servicosCake
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Position overview Position title: Moore Postdoctoral Fellow Salary range: A reasonable salary range estimate for this position is $69,073 - $71,632. The posted UC academic salary scales (https
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international work environment Learn more about CQT at https://www.cqt.sg/ Job Description The candidate will work on analytical aspects of quantum information theory, e.g., exploring entropic information
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protects the privacy of your personal data. Student privacy rights are governed by the Family Educational Rights and Privacy Act (“FERPA”), as described in Section 2.7 of Rights, Rules, Responsibilities
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees