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Admission requirements: Bachelor degree in Physics. Additional optional skills and qualifications: Previous experience with numerical simulations and data science. Contracting requirements: Presentation
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information can strengthen community networks and encourage adaptive behaviors in a warming urban environment. Key Responsibilities • Conduct multi-scale multi-physics numerical simulations, e.g. simulation
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experience in Mechanical Engineering, Applied Mathematics, Physics, Acoustics, Computational Science or a closely related discipline. Strong experience in numerical/computational modelling (e.g., FEM
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working with numerical weather prediction (NWP) or Earth system models such as WRF or the Unified Model. Strong preference will be given to candidates with experience in next-generation frameworks like
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the career path for numerous to enter clinical development. Two fellowship position will be recruited for in the 2026-2028 cycle: 1. PharmD Fellow – located at the Boston, MA or Gaithersburg, MD office 2. PhD
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additional collaborators including Prof. Alexander Babanin (University of Melbourne, Australia), will form a team of supervisors for the PhD fellow. The research will involve running customized numerical
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written and oral communication skills Proficiency in specific programming languages, numeric and analytical skill Competent in soft skills / general personality traits e.g. information visualization
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close proximity to numerous restaurants and cultural attractions. Why you’ll like working with us: At the Wyss Institute, you’re a member of a supportive, dynamic community that is united by its shared
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oral communication skills Proficiency in specific programming languages, numeric and analytical skill Ability to work independently / develop solutions under strict timelines, meticulous and eye
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month 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