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integration of heterogeneous data sources. Experience with signal processing and statistical modeling of high-dimensional data. Strong programming skills in Python and relevant ML frameworks (PyTorch
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decision is made. Strong experience in deep generative models (e.g., diffusion models, VAEs, transformers) Programming proficiency in Python and PyTorch Interest in multimodal human communication and virtual
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working with large data sets. Strong programming skills (e.g. Python, PyTorch) Ability to work both independently and collaboratively in a multidisciplinary team. Preferred qualifications A doctoral degree
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ability to handle large and complex datasets, including preprocessing and integration. Strong programming skills (e.g., Python, R, MATLAB, or similar). Demonstrated ability to conduct independent research
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Optimal Control Theory Strong programming skills in C++/Python/MATLAB Familiarity with parallelization and high performance computing (CPU and GPU friendly code) Experience with Machine Learning, generative
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ability to comfortable processing these data using tools like Seurat, Scanpy, or QuPath. Proficiency in Python or R, coupled with familiarity with machinelearning frameworks (e.g., scikit‑learn, PyTorch
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good understanding of all the main steps in crystallography-based structure solution is important Good programming knowledge, particularly in Python and/or C/C++ Ability to cooperate and work in a team
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like Python or C/C++ Familiarity with networking and security protocols is a plus You are expected to be somewhat accustomed to teaching, and to demonstrate good potential within research and education
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publication record Ability to develop systems using programming languages like Python or C/C++ Familiarity with networking and security protocols is a plus You are expected to be somewhat accustomed to teaching
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fluid dynamics and vascular modeling in microenvironments Skills in data analysis and image processing (e.g., Python, R, ImageJ) Ability to mentor junior researchers and contribute to team leadership What