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Field
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particular 2D/3D radiation-hydrodynamic simulations using high-performance computing (HPC) and comparison to observational data. The positions are fixed-term for two years and start immediately, preferablyno
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a postdoctoral fellow at the University. The individual cannot have held previous positions in the professional ranks. Preferred Qualifications Experience with NGS data analysis and Linux based HPC
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-performance computing (HPC) systems. Proven track record of interdisciplinary collaboration on research projects. Strong publication record in relevant fields such as environmental science, data science, or
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(HPC). • Experience with machine learning and network biology. • Background in cancer research. Pay Range: $62,232.00 - $81,000.00 To apply visit https://academicjobsonline.org/ajo/jobs/31509 and submit
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‑seq or other MPRA approaches; integration of enhancer activity with 3D genome features. Prior work on drought stress responses, wood development, or conifer systems (spruce/pine). Proficiency with HPC
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 2 months ago
programming skills in languages such as Python, C/C++ and CUDA # Familiarity with modern deep learning frameworks like Tensorflow 2.x.x, PyTorch # Mandatory experience with High-Performance Computing (HPC
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coastal hub. Read more about working at Chalmers and our benefits for employees. AIMLeNS offers: Access to state of the art HPC computing facilities (BerzeLiUs, Alvis) Mentorship for career development
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Center for Advanced Systems Understanding, Helmholtz Center Dresden-Rossendorf | Germany | about 2 months ago
Computing (HPC) cluster environments and the Slurm cluster environment is required Communication skills in English and in a professional context (presentation of research results at scientific meetings
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The Advanced Photon Source (APS) (https://www.aps.anl.gov/ ) at Argonne National Laboratory (Lemont, Illinois, US (near Chicago)) invites applicants for a postdoctoral position to develop and
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energy efficiency during the training process and (ii) science yield (sensitivity / resolution) during inference. A key aspect is to benefit from hybrid HPC + AI approaches within the workflows