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Field
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skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A certain affinity towards turning complex concepts into real-world practice is desired. The successful candidate is
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to evaluate and inform digital health interventions for women at increased risk of GDM. The project will primarily utilize data collected from a completed randomized controlled trial (https://bump2babyandme.org
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degree in bioinformatics, computational biology, statistics, or a comparable field Strong programming skills in common analysis environments such as R and/or Python Experience in the analysis of single
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validation on simulated and real data Skills Abstraction capabilities, strong programming skills in C/C++ and Python, collaboration skills. English is needed. REFERENCES [0] C. Bekker, S. Rothmann, M. Kloppers
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in Python and affinity with large geospatial datasets. Interest in interdisciplinary research at the interface of geoscience, engineering, and societal impact. Good communication skills and willingness
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, or computational imaging; You have strong skills in digital signal processing (DSP), including familiarity with machine learning or deep learning; You have proficiency in scientific programming (e.g., Python, MATLAB
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Aerospace Engineering, Aeronautics or a comparable degree, thorough knowledge of AI/ML methods, acoustics, and air traffic management are preferred, as well as excellent programming (Python, Java, C++, …) and
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materials will also be appreciated. Advanced knowledge in scientific programming (e.g. Python, Matlab) is also required. LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelGood Research FieldEngineering
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-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent
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. Strong research background in biometric security, presentation attack detection, or digital identity systems. Demonstrated programming skills in Python, MATLAB, or C++, and experience with deep learning