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, Stata, Python, R). Conduct cross-city comparative research Map and analyse the geographical distribution of eviction patterns within and across cities; Examine similarities and differences in urban
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research methods, ideally in numerical optimization and simulation models Proficiency in one of the major programming languages, such as Python Commitment to participate in the design and implementation
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to develop simulation models and analyze complex system performance under realistic conditions in one of MATLAB, Python, C/C++ Strong analytical thinking, problem-solving skills, and ability to work
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programming or scripting skills (e.g., Python, MATLAB, Bash) are a plus Prior experience in amyloid biology, protein aggregation, or disease-related structural research are an advantage. Ability
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strong background in shape modelling, deep generative modelling (diffusion/transformers), or multimodal representation learning. You have strong programming skills, especially in Python, and preferably
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models), programming skills in Python or a similar programming language (experience with PyTorch or another machine learning library is required), at least a basic expertise in user experience (UX) and
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biology, bioinformatics, genomics, statistics, physics, or a related quantitative field Demonstrated ability to analyse large-scale sequencing data using R, Python or equivalent A self-motivated team player
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, GNSS, or satellite communications is a plus Strong programming skills in MATLAB is required. Experience with C/C++, Python, or related tools is advantageous Ability to work independently and
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify microplastic particles based on spectral and morphological fluorescence data. Develop and maintain modular analysis pipelines in Python