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, generative models, or equation discovery — rather than traditional statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch
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of C, C++ and Python programming. Knowledge of Linux OS and the related programming environments. Excellent skills with real time operating systems such as Xenomai or other RT OS. Excellent understanding
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/Engineering, Information Sciences, or related field. A minimum of 5 years of relevant professional experience. Strong Linux/UNIX skills. Proficiency in shell scripting and Python. Experience with configuration
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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together to find multidisciplinary opportunities for improvements across design and technology. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/device-and-technolog
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/C++, Fortran, and/or Python Proven experience with multiple parallel programming paradigms, including but not limited to; MPI, OpenMP, and CUDA (Compute Unified Device Architecture) Experience in a
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, genetics, or related field • Expertise in genomic data analysis, particularly single-cell RNA-sequencing and/or alternative splicing • Strong background in bioinformatics and programming skills (e.g., Python
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symptom trajectories and clinical outcomes (AMBIENT-BD)”, https://www.ambientbd.com/). The post holder will be a key member of the Data Analytics Research and Technology Research group (DARTH, https
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, or machine learning Solid mathematical and physics background, distinct analytical skills Very good programming (Python, C++) and computer (Linux, Windows) skills Excellent cooperation and communication skills
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programming (Python, C++) and computer (Linux, Windows) skills Excellent cooperation and communication skills You enjoy to work within a diverse team in an international and interdisciplinary environment Very