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Field
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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groups settings. Ability to coordinate with faculty, graduate and undergraduate students, and external collaborators. Skills in parallel computing, parallel application design and development, and
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challenges facing the nation. The Computational Coupled Physics (CCP) Group within the Computational Sciences and Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity
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almost limitless repertoire of movements, from brewing coffee to parallel parking. To achieve this, the lab uses a wide range of methods, including computational modeling, psychophysics, patient testing
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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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Deadline 7 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by
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patients and non-human primates are conducted using identical behavioral paradigms and combined with computational approaches. We are seeking an extremely motivated postdoctoral researcher with background in
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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Mathematics, or a related field, awarded within the last five years Programming experience in one or more of Python, C++, Fortran, or Julia Knowledge of high-performance and parallel computing Experience