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Field
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strong background in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python
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(e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C++ ability to work independently and collaboratively in
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will be responsible for programming and maintaining gait exoskeleton systems, developing and implementing real-time control algorithms in C/C++, Python, and Simulink, as well as integrating feedback from
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/Python pipelines for EEG analyses, preparing manuscripts and high-quality visualisations, and overseeing ethical approvals and participant recruitment as required. About You You have or be close to the
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programming skills (e.g. Python, R, ...). - Previous experience with model-based fMRI experiments, advanced multivariate data analysis and computational modeling is desirable. - Fluency in English Additional
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. The postdoctoral researcher will share is working time on both laboratories. Expected Skills Experience in analysis of data of gamma-ray telescopes Knowledge of Python, C++ Working knowledge of English (B2 / C1
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Qualifications MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications Proficiency in Python, MATLAB, and R, combined with experience in signal and image
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models and machine learning. Experience in epigenetics or gene regulation is a plus, and experience with statistical analysis tools such as R or Python is recommended (successful candidates will be
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; - Installing, maintaining, and monitoring calculation codes on GENCI platforms and the local cluster; implementing Python tools for automating CSP/DFT calculations; - Participating in the scientific activities
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experience in handling large metagenome datasets (first author publication/s in the topic are required). Relevant experience in R and/or python programming (eg. pipeline development for metagenomic data