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proficiency in coding, at least using Bash and Python. Applicants should maintain their code in a public repository (e.g. GitHub) and include the link in their application. Proven skills in Linux/HPC
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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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mitigation approaches Familiarity with various mitigation regulations and guidelines Familiarity with computational tools such as Python You should also have good interpersonal and communication skills and
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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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development, spin simulations, or MR signal modeling is highly desirable. Strong programming skills, for example in Python, MATLAB, C/C++, or a comparable scientific computing environment. Experience with
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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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including transformers, self-supervised learning, foundation models, autoencoders or related architectures; strong programming skills in Python and experience with a deep-learning framework such as PyTorch
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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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architectures; • strong programming skills in Python and experience with a deep-learning framework such as PyTorch, including training and evaluating models on GPU/HPC infrastructure; • experience working with