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completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field Strong programming skills in Python, R, or Julia, with
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Environmental Engineering or a related field High proficiency in programming languages (e.g., Python or MATLAB) A strong record of authorship in peer-reviewed academic journals Excellent interpersonal and
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biology methods, inorganic spectroscopy, metabolomics, proteomics, aqueous phase catalysis, analytical methods (mass spectrometry, NMR). Experience in data analysis and scientific programming (Python
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written
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scientific discipline. Experience in developing and running an atmospheric transport model. Experience in interpreting data. Experience with FORTRAN and Python computer languages. Proven ability to work
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro
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programming language (preferable: MATLAB or Python) Experience with one or several of the listed human neuroscience techniques. Ability to work and learn independently and perform research with a high level of
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spatial transcriptomic data using R or Python; interprets findings in the context of transplant immunobiology. Establishes new research protocols and procedures: Develops and optimizes protocols
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in developing engines and/or fuel cells models in GT-Power. Experience in using MATLAB/ Python/Siemens Simcentre to develop AI-assisted programme and models for powertrains and propulsion systems