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Andres Masegosa (arma@cs.aau.dk), Department of Computer Science. (please see: https://andresmasegosa.github.io/ . The project’s domain PI is Professor Jamal Jokar Arsanjani (jja@plan.aau.dk), Department
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machine learning. Strong technical background, including substantial experience with programming in Python and relevant ML/statistics libraries (e.g. PyTorch, Darts, Hugging Face Transformers, Pyro, PyMC
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++ and Python are required. Experience with control and optimization of power generation systems (e.g., wind power) is desirable but not required. Some experience with real-time control hardware is
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relevant field Proficiency in Python, and/or R statistical package. Proficiency in using LLM, model training, and Generative AI. Experience with supervised and unsupervised ML techniques Skill in collecting
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data analysis (e.g., MRI preprocessing, statistical modeling, familiarity with tools such as FSL, SPM, AFNI, or similar platforms). Proficiency in statistical programming (e.g., R, Python, MATLAB
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., MRI preprocessing, statistical modeling, familiarity with tools such as FSL, SPM, AFNI, or similar platforms). Proficiency in statistical programming (e.g., R, Python, MATLAB). Additional Information
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Associate, Lovejoy Center Posting Number req24906 Department AZ Inst for Resilience Department Website Link https://lovejoycenter.arizona.edu/ Location Main Campus Address Tucson, AZ USA Position Highlights
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Qualifications – Experience, Education, Knowledge & Skills Data Science Experience: Proficiency in using R or Python for phylogenetic comparative methods and high-performance computing (HPC) environments
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research area: Python or C#, robotics, additive manufacturing, finite element analysis, or computational fluid dynamics. Experience in teaching and co-supervising thesis students at university level
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-quality software. Strong software development skills, particularly in Python or C++, with experience in data analysis, simulation, or workflow scripting. Excellent verbal and written communication skills