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for general-purpose workloads in standard distributions, and that interact in poorly documented ways with the Python runtime, its garbage collector, and the memory allocation patterns of data science libraries
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Inria, the French national research institute for the digital sciences | Nantes, Pays de la Loire | France | 39 minutes ago
cyberinfrastructure platform to bridge data collection, datasets catalog and online development of workflows; Implementation of distributed services for computation in the form of a python library that allows combining
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infrastructure for different researchers within and across teams. Able to perform in-depth troubleshooting and tracing of traffics / activities across various cloud services within commercial cloud environment
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that are contributed by BON in a Box collaborators. This includes understanding the analysis with support from publications, the team and the contributor, testing pipelines with different parameters, fixing R and python
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(ISAC) Implementation of a DSP library in Python Technical documentation and maintenance of the software Experimental verification of the software via simulations Working with our hardware team
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Investigators and their teams to create analytic datasets in SQL server. This will require gathering specifications and listening to requirements from various teams. The position must be responsive to different
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biology or a closely allied discipline along with experience in creating, writing, enhancing, and running code in R/Python, using and developing microfluidic devices and advanced imaging. You will have
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experience in histology, microscopy, or immunostaining Basic data visualization skills in Python As a person, you are collaborative, proactive, and flexible, with the ability to adapt to different project
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configuration of physics-based simulation environments (e.g., different simulators and robot models) Modeling and implementation of contact-rich handling and assembly operations in simulation Design and training
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transformations, geometric corrections, raster-vector conversions, attribute joins, and data cleaning using tools such as QGIS, ArcGIS, or R and Python geospatial libraries (e.g., R's sf and terra packages