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. Ability to develop, understand, and critically evaluate machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal
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have: MSc in engineering or similar discipline by the start date of the position Experience with mechanical modeling and simulation Experience in computer programming/scripting (e.g., C++, Python, Matlab
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programming skills are a plus: MATLAB, Python, Git Fluency in English An open personality and good communication skills in written and spoken English. TU Delft (Delft University of Technology) Working at TU
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Science, Biostatistics or a closely related discipline; have demonstrable experience with training machine and deep learning models, preferably using Python; have a basic understanding of biology and/or
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possibly measurements. You will mainly do your programming work in a mixed programming environment, i.e. combining both high-level and low-level programming languages such as Python for the former and
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Python and modern machine‑learning development, including version control (Git), testing, and reproducibility; experience with cloud-based solutions (e.g., Azure) is a plus. In our international working
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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skills. experience in data analysis, quantitative modeling and programming (e.g., R, python); knowledge of nutrient and/or agrochemical cycles in agriculture; excellent scientific writing skills in English
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data science, biomedical engineering, technical medicine, or a related field. You should have strong programming skills (Python, PyTorch), deep learning knowledge (multimodal learning, longitudinal
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related field; a solid understanding of molecular genetics, glial and myelin biology and rare neurological diseases; experience with either iPSC culture and differentiation or programming (Python and/or R