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is part of the Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), and located at the main Campus in Odense, at the core of the Odense Robotics Cluster involving 120+ startups
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developing and implementing very large deep learning models. Familiarity with high performance computing environments (e.g., HPC clusters, GPUs, Cloud resources) and managing Linux based hardware systems
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. Applicants should demonstrate expertise in programming languages such as Python and/or R, as well as experience working with high-performance computing clusters. The successful candidate will be a team player
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access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale, and together with access
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these applications to inform new foundational ML and NLP innovations. The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE leadership computing
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Cluster and DOE leadership computing facilities. Access to these platforms will allow computing at scale, and together with access to unique data sources, will ensure that the successful candidate has
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. Developing bottom cells at different scales (from 1 by 1 cm2 to 6 by 6 inch2) utilizing the PECVD-PVD cluster. Performing device characterization, and modeling aimed at champion PCEs. Manage project tasks
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and uses these applications to inform new foundational ML and NLP innovations. The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE
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Summary The primary research focus is the role of cell adhesion and neuronal self-avoidance in the developing mouse retina, focusing on two families of cell adhesion molecules: the clustered
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(e.g. Python, R, …). Familiarity to work on a Linux computing cluster (HPC). Preferably experience in working with large medical image data. Vivid interest in the analysis of microscopy images or similar