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: Essential criteria A doctorate (or will shortly satisfy the requirements of a PhD). The doctorate must be in a relevant discipline area, such as statistical machine learning, computational and quantitative
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engineering aspects to the project (around model validity) and computer science aspects (around parallel efficiency and computational time). You will be working closely with engineers at Rolls-Royce and with
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English skills, good ability to write scientific publications)?Our offerSuccessful candidates will join a dynamic, international and world-wide highly regarded research team. As fully funded PhD students
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properties (including structural, defect, electronic, ion transport, surfaces) and will link up with parallel experimental activities. Applicants should have a PhD in chemistry, physics or materials science
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technologies, drug discovery and advanced therapeutics. We have excellent facilities to support our parallel use of pre-clinical cell and in vivo models, alongside human cellular models, which provide
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these codes in C++ or Fortran Adopting these codes for multiple-CPU and/or GPU platforms via parallelization schemes. Validating these codes via canonical and real-world examples. Job Requirements: PhD in
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, graduate and undergraduate students, and external collaborators. Skills in parallel computing, parallel application design and development, and performance evaluation and optimization. Knowledge and
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 26 days ago
. The use case is a electrical substation implementing a process bus architecture conformal with IEC 61850 specifications. This includes redundant PRP (Parallel Reduncdancy Protocol) network architectures
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. Additional duties will include consulting some the tasks conducted by PhD students in our team. Enquiries regarding the role or the recruitment process can be addressed to prof. Agnieszka Janiuk (agnes
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in