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analysis of complex datasets using specialized peptidomics tools. Experience in MS data analysis using software such as FragPipe and DIA-NN, as well as basic programming skills in Python. Practical
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. In addition, the following are requirements for the role: Strong programming and quantitative skills, particularly in Python and/or R. Experience in deep learning, machine learning, or large-scale
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MRI data processing and analysis. Knowledge of cardiovascular physiology. Experience programming in MATLAB and Python. Experience in systems biology, mathematical modelling, and digital twins
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field, such as environmental science, economics, sustainability science, or a closely related field. Strong quantitative skills: modelling, data analysis, and scientific computing (e.g. R, Python). Strong
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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of the following areas will be beneficial. Experience with machine learning or data-driven modelling, particularly for chemical or process systems Experience with Python, or similar tools for data analysis and
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models and/or probabilistic modelling, and excellent programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are required. Excellent skills in spoken and written English
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language (C++, Python, Rust, …) One high-quality first-author paper (journal or top-tier conference) You are expected to be somewhat accustomed to teaching, and to demonstrate good potential within research
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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. Experience and expertise in human mobility simulation and prediction with agent-based modeling and deep learning techniques. Proficient in Python programming for geospatial data processing, modeling, and