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in this position is contingent upon adequate grant funds. Position Status Full Time Posting Number 26FA0741 Posting Open Date Posting Close Date Qualifications Minimum Education and Experience A PhD in
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to software releases and reports, and mentor or collaborate with students and researchers as appropriate. We are looking for: PhD degree, within the last 3 years, in Computer Science, Computational Science
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Job Description Here's a Glimpse of the Job The Postdoctoral Research Associate will be involved in developing deep learning architecture for multi-object data integration, federated learning approaches
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independence in a highly collaborative environment that values mentorship, innovation, and professional growth. Be Bold. Join a research program focused on understanding how tumors evolve during and after
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engineering, precursor chemistry, energetic band alignment, low-trap-density CQD solids, and novel infrared photodetector architectures. The first position focuses on CQD synthesis and surface chemistry
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an exceptional Postdoctoral Research Associate in Healthy Homes who has: a PhD or near completion in architectural science, building physics, or similar fields. demonstrated research expertise and an emerging
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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by ATi/Innovate UK and Rolls Royce and is fixed-term to October 2029. You will join a world‑leading programme advancing experimental and numerical methods for predicting the performance of 3D woven
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | about 1 month ago
and organoid systems that capture key aspects of tissue architecture, cellular heterogeneity, epithelial-stromal interactions and disease-associated states (Dowbaj et al., Nature 2025; Yuan et al
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by ATi/Innovate UK and Rolls Royce and is fixed-term to October 2029. You will join a world‑leading programme advancing experimental and numerical methods for predicting the performance of 3D woven