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field Strong programming skills (Python) and experience with deep learning frameworks (e.g., PyTorch, TensorFlow). Familiarity with satellite data and geophysical modeling. Interest in physically informed
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., C/C++, Python, Java, Go, Rust) A proactive attitude toward bridging research and practical implementation Sound publication track record in relevant international conferences and top journals
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Control / Estimation Theory Intermediate programming skills in Python and C++ Intermediate level skills and proven experience with PyTorch Working knowledge of ROS and previous experience of handling
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, for instance, R and/or Python and you are fluent in English. To be eligible, your PhD defense must be at most 5 years ago at the beginning of the postdoctoral fellowship and you cannot have resided or carried
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, preferably in Python or R, and experience in the Linux environment Experience with large-scale data analysis, such as genomics or transcriptomics data Experience with a workflow management system such as
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aspects of Cultural Heritage Science and the challenges inherent to this field (experimental noise, few-data regime) will have to deploy deep learning algorithms using Python/Keras/Pytorch or equivalent
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strong motivation to work in a multidisciplinary team; Experience with quantum mechanical software packages (VASP, CP2K, …), machine learning potentials (SchNet, NequIP, MACE), and coding (Python, C
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in LaTeX and good programming skills (typically in one of R, Python, or Julia, with the capacity to learn). Familiarity with, and readiness to apply, the principles of open research, including code
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skills in at least one relevant language (e.g., Python, R, Rust, JavaScript) Familiarity with smart contract interaction (e.g., web3.py/ethers.js, Solidity/Vyper, sub-graph indexing), and local development
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the Global South is a strong advantage. - Good knowledge of statistical methods and relevant software packages (e.g. R, Python). We also encourage candidates who do not meet every single requirement but