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, Integrate the DNAm datasets with longitudinal eHealth records to build epigenetic signatures of healthy ageing, which we will then track longitudinally in parallel with other hallmarks of ageing using six
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would be doing: You will plan and conduct research, helping you refine your research interests and identify which area best aligns with your clinical background and long-term academic goals. In parallel
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should have or be close to completing a PhD in aerospace engineering (or equivalent qualification and experience), with experience in fixed/rotary wing air vehicles. What you need: Strong understanding
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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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for large samples at ESRF ID16A using multislice tomography approaches. You will lead the development of and work with parallelized computer models to simulate how coherent waves travel through materials with
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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of capabilities such as AI training and inference at scale, and tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics
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PhD) EUR 3,000 Starting package from EUR 10,000 to EUR 20,000 per year Applicants will be encouraged to apply for complementary external funding (e.g., MSCA or EMBO fellowships) in parallel to/following
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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, reproducible computational workflows for large-scale screening and multiscale model integration. HPC at Scale: Run, optimize, and profile large simulations on HPC systems; ensure efficient parallel performance