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, robust/distributed control, data-driven identification/control, numerical optimisation. Strong programming skills in at least two of the following: Julia, MATLAB, C/C++, Python. Demonstrated ability
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characterization of deep-water habitats, GIS spatial analysis of species distribution data, and quantification of ecosystem services. Preference will be given to applicants that possess a diverse set of skills and
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of seismic methods and numerical simulations, Good PC and programming skills (e.g., with Python, MATLAB), Experience with measurement techniques and field measurements using sensor technology (ideally using
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, environmental data science, or a closely related STEM discipline Demonstrated expertise in urban spatial data analytics, with proficiency in GIS software (e.g. QGIS, ArcGIS) and geospatial methods Experience in
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. You have strong programming skills in C/C++, Python, and/or Matlab. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP, ADALM-Pluto) and software (e.g
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Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl
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. Experience with high-throughput data analysis, including single-cell RNA sequencing, spatial transcriptomics, or computational immunology using R or Python, is a plus but not required. Excellent organizational
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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise your own workload
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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise own workload and
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or machine learning. Excellent programming skills (e.g., MATLAB, Python, or ROS), a strong publication record, and an ability to work collaboratively in multidisciplinary environments are essential. Prior