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complex traits. Dr Speed has developed the software package LDAK (www.ldak.org ). The position will be mainly funded by an ERC consolidator grant, aimed at finding novels ways to classify complex diseases
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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Simulations (INMA, Zaragoza) Build and maintain software infrastructure for modeling quantum systems with machine learning tools. Investigate neural-network-based representations of many-body quantum states
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architectures) to large-scale biomedical datasets. These models will be used to work with different types of data from the healthcare and biological domains, including genomic profiles, and clinical event
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hyperspectral — to support early warning, strategic situational awareness and the monitoring of emerging risks. A key focus of the fellowship will be the development of explainable AI architectures capable not
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synchrotrons and x-ray free-electron lasers. Key Responsibilities Perform electronic-structure calculations using ab initio quantum chemistry methods and software (commonly CASSCF-based approaches) Investigate a
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, and emerging applications, such as stateful serverless workflows and agentic AI. The project takes a clean-slate approach to cloud infrastructure by exploring new architectures, abstractions, and
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other software tools Develop, test and implement data pipelines Required Knowledge, Skills, and Abilities: Required PhD in geosciences, computer science, engineering or related field Proven research
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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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in professional organizations Basic Qualifications: A PhD in Mathematics, Applied Mathematics, Computational Science, or a related field completed within the last 5 years Preferred Qualifications