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real-time high-speed implementations Use machine learning to improve discrete modulation formats Develop MATLAB or python code Experimentally demonstrate the developed algorithms and methods to evaluate
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: PhD in computer science, machine learning, operations research, transportation engineering or a related field. Programming skills in C/C++ and Python, along with experience working with simulation
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digital co-simulation platforms (e.g., Modelica-Python/Simulink) Applying machine learning and data-driven approaches to enhance the operation of district heating substations Participating in course
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co-simulation platforms (e.g., TRNSYS-Python) Implement and test AI-enabled smart energy management strategies in real-world settings Conducting in situ measurements, including planning, setting up
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or related field. Expertise in graph machine learning and demonstrated experience in multi-omics data integration. Strong programming skills in Python and its scientific and graph ML libraries (numpy, pandas
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on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use. However, powerful as it is, MagTense is at present limited in its
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considered an asset in these positions. Applicants must also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab
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modelling, preferably WRF Experience of scientific programming and running code on HPC systems Experience with Fortran, Python and Linux Shell Any of the following is advantageous but not essential
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. Develop data processing pipelines covering drift correction, sensor I/O, and visualization implemented in Python. Active dissemination of the results in scientific articles and participation in national and
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related field Experience with Earth System or biogeochemical modelling Strong programming skills (e.g., Python, R, MATLAB) and experience with advanced machine learning modelling A strong interest in