Beschreibung
We are currently looking for a Python developer with 1-2 years experience. Someone who has computer science degree.
Role is hybrid in Nuremberg area.
- Implementation of specified pipelines to process raw data from the lab equipment.
o This will be done in Python, depending on the computational complexity, some C/C++ code might be required or needs to be integrated.
o Processing and analysis of complex time-series data.
o Conversion of data into different coordinate systems
o Re-sampling of data in case of sampling rate differences
o Integration of algorithms with the software from different lab-machine vendors.
o Utilization of a REST-API to upload the processed data into our ADM system.
- Depending on the use-case, deployment of the pipeline can differ:
o Integration of algorithms into the “Plotly-dash” application.
o The pipeline can also be part of a Notebook on Databricks.
- Implementation of visualization approaches to make it interpretable by sports scientists:
o Visualization complex data in an intuitive way
o Utilization of Matplotlib/Plotly for plotting data
o Utilization of 3D rendering frameworks to render time dependent 3D data
- Implementation of data-fusion approaches:
o Time-wise registration of data from multiple sources with varying sample rate. All data shared by us upfront.
o Registration of datasets acquired in different spaces.
Role is hybrid in Nuremberg area.
- Implementation of specified pipelines to process raw data from the lab equipment.
o This will be done in Python, depending on the computational complexity, some C/C++ code might be required or needs to be integrated.
o Processing and analysis of complex time-series data.
o Conversion of data into different coordinate systems
o Re-sampling of data in case of sampling rate differences
o Integration of algorithms with the software from different lab-machine vendors.
o Utilization of a REST-API to upload the processed data into our ADM system.
- Depending on the use-case, deployment of the pipeline can differ:
o Integration of algorithms into the “Plotly-dash” application.
o The pipeline can also be part of a Notebook on Databricks.
- Implementation of visualization approaches to make it interpretable by sports scientists:
o Visualization complex data in an intuitive way
o Utilization of Matplotlib/Plotly for plotting data
o Utilization of 3D rendering frameworks to render time dependent 3D data
- Implementation of data-fusion approaches:
o Time-wise registration of data from multiple sources with varying sample rate. All data shared by us upfront.
o Registration of datasets acquired in different spaces.