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register on the electronic platform https://www.apply.up.pt to submit their application, selecting the procedure they wish to apply for (Ref. INVTERM-FFUP-26-4). The application is completed by filling in
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the Institute of Planetology and Astrophysics of Grenoble (IPAG, France). The successful candidate will join the group led by Benoît Cerutti to work on ab-initio plasma simulations of relativistic
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Location(s) Number of offers available1Company/InstituteInstitute of Theoretical Computer ScienceCountryPolandCityCracowGeofield Contact City Kraków Website http://www.matinf.uj.edu.pl/ Street ul
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Posting Details Student Title Classification Information Quick Link https://chapman.peopleadmin.com/postings/39392 Job Number SE187724 Position Information Department or Unit Name Dodge College
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group and Responsible Artificial Intelligence group at the Department of Computing Science. More info about the research groups: https://www.umu.se/en/department-of-computing-science/research/research
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of the mobility platform with computer vision and AI. Applicable Legislation: Labour Code, approved by Law no. 7/2009, of 12 February, in its current reading. The selection panel shall be Prof. Dr. Susana Sargento
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. For additional information about the Liao research group, please visit: https://sites.google.com/view/chentingliao/ Department Contact for Questions: Questions regarding the position or application process can be
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mechanisms using computer simulations and the methods of non-equilibrium statistical physics. The Research The PhD student will work under the supervision of Dr. Philip Bittihn within the European Doctoral
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Assistant professor position (non-tenured) in molecular dynamics simulations of plant hormone tra...
expertise in molecular dynamics simulations on membrane proteins. The position is advertised for 2 years of employment and with a start date in October 2026. Expected start date and duration of employment
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 29 days ago
of numerical simulations and digital twins. By using advanced machine learning methods, such as Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (vPINNs), the project