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auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application Application files should contain a resumé, an application letter and grade records of the 2 last
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. Required Skills and Candidate Profile The project is intended for a candidate with: ➢ Skills in medical image processing and deep learning adapted to clinical applications. ➢ A good knowledge of Python
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, clustering, classification • deep learning, variational auto-encoder, back-propagation, • knowledge of R (main programming language), Python and C++. Application: Application files should contain a resumé, an
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, Statistics, Epidemiology or related disciplines Experience in handling longitudinal and birth-cohort datasets Extensive experience in quantitative research (Stata, R, Python, etc.) Fluent in English (speaking
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PhD degree in Computer Science, Physics or a related field Experience with parallel programming models Strong programming skills in C/C++ and/or Python Knowledge of distributed memory programming with
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software development, with a focus on full-stack development Technical Skills Strong proficiency in Python 3 Flask/Django or other MVC framework HTML/CSS/JavaScript Experience with modern JavaScript
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. Experience or good knowledge of analyzing other types of biological data. Knowledge of Python. Experience in leading research projects. Other expected qualities: Teamwork skills. Strong collaboration skills
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: Programming in Python and/or R Data science (e.g., tidyverse, pandas) Machine learning (e.g., scikit-learn) Deep learning (e.g., PyTorch, Keras3) (Optional) bio-signal processing and brain imaging (e.g., EEG
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should be proficient in Python, PyTorch or Scikit-learn. The candidate should also be endowed with a strong passion for multidisciplinary studies and all aspects of research ranging from fundamental work
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master’s degree in mathematics, physics or informatics with a strong knowledge in machine learning. Skills: Coding in Python and/or R is required. Previous knowledge in archaeology and zoo-archaeology would