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. The initiative is coordinated by DTU National Food Institute and is conducted in collaboration with research groups at Aarhus University, Aachen University and Chalmers University of Technology. We believe that we
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and applying new skills. Experience with programming in Python and a working knowledge of statistics It would further be beneficial if you have some of the following skills: Hands-on experience with
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is
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, or reinforcement learning Proficiency in Python and contemporary software ecosystem for optimization, dada management, and API development Experience with teaching and supervision Experience with IoT, edge computing
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or Python) to the level that you can set up, carry out, and automate Comsol models of magnetotransport independently. Ability to work independently, to plan and carry out complicated tasks, and to be a part
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simulation models (e.g., EnergyPlus, IDA ICE or similar). Good understanding of building physics Proficiency in programming e.g. in MATLAB, Python or similar environments for modelling and data analysis. A
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-reviewed journals Experience with one or more scripting language (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can biologically interpret results and relate to breeding decision making
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simulation (e.g., Python, MATLAB). Prior experience with industrial research collaborations and the patent filing process is a plus. Qualification requirements Appointment as postdoc requires academic
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electricity markets, in particular intraday, ancillary, and spot markets Proficiency in Python, MATLAB, Julia, and GAMS for data analysis and optimization Experience with price forecasting, demand modelling
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Solid programming experience, preferably in Python Familiarity with structured data handling (e.g., SQL) and scientific workflows Documented experience with ontology development, knowledge graphs, and