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Deadline 1 Apr 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 35 Is the job funded through the EU Research Framework Programme? Other EU
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description A call is open for the award of one (1) Research Grant
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. The Department of CSD offers a Bachelor of Arts and a Master of Arts degree in CSD. There is both a residential and distance education component for the MA program in Speech-Language Pathology. The Department
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is available after 12 months of employment. A retirement program with a generous employer contribution and additional voluntary retirement programs (457 or 403b) are available. https://www.kumc.edu
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if the candidate is enrolled in a PhD program - art. 6º, n.4 c) https://diariodarepublica.pt/dr/detalhe/regulamento/950-2019-127238533 https://files.dre.pt/2s/2019/12/241000000/0009100105.pdf subject to suitable
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California State University, Northridge | Northridge, California | United States | about 2 hours ago
. Performs other duties as assigned. *NOTE: To view the full position description copy and paste this link into your browser: https://mycsun.box.com/s/57qp6bpggw7607zijzoft2k7pcnmsk5x Qualifications
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initiatives - Good command of English (involving good communication skills) LanguagesENGLISHLevelGood Additional Information Selection process To apply, please send: • A resume • A cover letter • A copy of the
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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will also specify the inaccurate or false information provided by the candidate in their application. Should the candidate be excluded from the recruitment process, they may file an appeal
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning for regularization with probabilistic generative