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Python and Java and be able to design and implement scalable software architectures for distributed and cyber-physical energy systems. Assistant Professor Applicants at the Assistant Professor level are
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skills in Python and experience with deep learning frameworks (e.g., PyTorch); Experience with distributed systems and edge AI; Strong publication record in reputable conferences or journals relative
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, or a related field Strong experience in spatial and/or landscape modelling Proficiency in R and/or Python Experience with GIS and remote sensing Ability to work with large and heterogeneous datasets
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quantitative genetics or animal breeding Has published high-quality research in peer-reviewed journals Experience with scripting languages (e.g., R, Python, SAS) and/or genetic software (e.g., DMU, ASReml) Can
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, or a related field Strong experience in spatial and/or landscape modelling Proficiency in R and/or Python Experience with GIS and remote sensing Ability to work with large and heterogeneous datasets
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, catalysis and/or surface science. For Topic 4, candidates must have documented skills within computational modelling of atomistic processes. Experience in scientific programming, e.g. using Python, is
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acceleration. Strong programming (C/C++/Python), data structures and software development skills. Track record of research excellence through publications or public repositories Good communications skills
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validation of energy system solutions will be an advantage. Strong programming skills in Python, MATLAB or similar environments are required, and it will be advantage if you have worked with hardware-in
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. Experience with digital twin modelling and validation of energy system solutions will be an advantage. Strong programming skills in Python, MATLAB or similar environments are required, and it will be advantage
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biology, pharmacology and/or immunology. Experience in statistical bioinformatics, including developing analysis pipelines and applying programming (R/Python) to genomic/transcriptomic data. Strong written