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Proficiency in running numerical coastal ocean models Familiarity with operating systems such as Linux/Unix and proficiency in shell scripting Strong programming skills, preferably in Fortran, C/C++, or Python
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or Python Machine learning methods (for the baseline prediction for the reward funds) is beneficial We expect: Strong motivation to contribute to policy-relevant research Strong interest in teamwork and
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Engineering, Computer Engineering, Computer Science, or a closely related field Strong background in robotics fundamentals: kinematics, dynamics, control, planning Proficiency in programming (C++, Python), and
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Experience in energy storage Experience with programming languages (ideally Python) Fluent in written and spoken English Very independent and self-motivated way of working but also excellent teamwork skills
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) is an advantage Experience in software tools such as Origin, Matlab, Python/Matplotlib or similar programs for data processing and evaluation. Knowledge of a relevant programming language complements
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of the above fields Solid methodological background in image processing and AI Strong publication record in relevant fields Hands-on programming experience in Python for data science, image processing, and using
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programming skills (e.g., Python, R, C/C++) and experience with high-throughput sequencing data. Solid understanding of RNA biology, transcriptomics, and epitranscriptomics. Desirable Skills Experience with
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. Strong programming skills, preferably in Fortran, C/C++, or Python. Competency in visualizing and analyzing large-scale climate datasets using software tools like Matlab, IDL, Ferret, Python, or R. Merit
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Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience with high-performance computing (HPC
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Biology, Physiology, or a closely related discipline Demonstrated expertise in molecular biology techniques (including metagenomics), bioinformatics and coding skills (R, Python, or equivalent) Proven