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Field
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. The candidate should have relevant scientific publications, showcasing research interest and experience in the core areas of the position The candidate must be proficient in Python, C++ or Java and familiar with
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simulations (LAMMPS, GROMACS). • Strong background in statistical mechanics and Python scripting (PyTorch/JAX for ML applications). • Ability to independently design and execute research projects, with a track
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Python or a similar system. A4 Knowledge of integrating Design-Make-Test-Analyze (DMTA) cycles for drug discovery with automated chemical synthesis systems. A5 Knowledge of automated solid phase synthesis
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projects The ability to carry out and publish high-quality research Knowledge on ROS, Python, C++ and Matlab The following qualifications will be considered an advantage when applicants are ranked: Practical
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the master's degree has been awarded. The candidate must have good knowledge in the topic of Cryosheric Processes. Proficiency in scientific coding and data analysis programming languages, such as Python
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the master's degree has been awarded. The candidate must have good knowledge in the topic of Cryosheric Processes. Proficiency in scientific coding and data analysis programming languages, such as Python
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. Proficiency in relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with
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postdoctoral experience). Proficient coding skills in at least one of Matlab, R and Python. Experience and proficient in processing different types of remote sensing datasets. Experience with terrestrial
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sequencing (NGS) and omics data analysis. Knowledge of microbial ecology, dysbiosis, and host-microbiome interactions. Familiarity with cell culture techniques. R, Python, or other data science tools
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. Strong programming and analytical skills (e.g., Python, R, GIS, modeling frameworks). Experience working with EO data, disease surveillance data, or socio-environmental modeling. Demonstrated interest in