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, Python) are expected as is experience in planning and executing fieldwork. Preferable you have experience with various telemetry methodologies to study fish behavior (e.g. PIT, radio, acoustic, and
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, or Python. You will also be able to shape your own research. This includes primary data collection through surveys, or qualitative or quantitative interviews. Working as a PhD student requires the ability
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correction and/or mitigation. Knowledge about networking protocols and distributed algorithms. Experience in programming, e.g., in C++, Python or Matlab. Experience with quantum simulators, such as NetSquid
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highly advantageous: Scientific programming in Python or MATLAB Probabilistic methods, Bayesian inference, or stochastic modelling Structural mechanics, material modelling, or multi-physics simulation Data
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: Scientific programming in Python or MATLAB Structural mechanics, reliability analysis, or probabilistic modelling Data analytics, SHM/SCADA data interpretation, or digital-twin technologies Wind energy systems
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analysis. Experience with programming tools like Python and energy system modelling tools like PYPSA is considered an advantage. The successful candidate should possess a curious and interdisciplinary
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wind energy systems. In particular, experience in one or more of the following areas will be considered highly beneficial: Scientific programming in Python or MATLAB Structural mechanics, reliability
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of advanced wind turbine components. Experience in one or more of the following areas will be considered highly advantageous: Scientific programming in Python or MATLAB Probabilistic methods, Bayesian inference
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bioinformatics including NGS (Nanopore, Illumina, PacBio) Experience with automation and coding in Python or other programing languages Experience with protein software tools like AlphaFold3, Boltz2, PyMOL
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning