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transcriptomics data using R/Python. Wet lab experience is beneficial but not essential. You have a strong enthusiasm for fundamental research and a scientific interest in the molecular genetics of immune cell
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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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., 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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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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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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, 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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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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, classification, and topic modeling). You are comfortable with programming and analysis in R, including tidyverse, reticulate, tidymodels, etc. You are comfortable programming in Python and working with relevant
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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