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documented research experience in a related field. The applicant should have experience in one or more of the areas/fields mentioned below Proficiency in programming languages such as Python or R. Experience
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Geosciences, Environmental sciences, Civil or Environmental Engineering, Physics or Mathematics or a related discipline Experience in programming (e.g., Python, MATLAB, or similar), interest in machine learning
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synucleinopathies Familiarity with multi-omics approaches, e.g., snRNA-seq Experience with image quantification software and/or basic data analysis in R or Python Experience working in interdisciplinary and
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methods, omics data analysis, and spatial tools is highly valued. Programming expertise in Python and/or R is essential. As a person you demonstrate high ambitions. You are equally innovative – and result
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Essential criteria are: Doctoral degree in computed tomography, image analysis, or similar Demonstrated experience in programming (e.g., Python, MATLAB) Theoretical knowledge and practical experience in
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experience, preferably in Python or R, and experience in the Linux environment Solid publication record in peer-reviewed journals. Proficient in verbal and written English. Excellent teamwork
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify microplastic particles based on spectral and morphological fluorescence data. Develop and maintain modular analysis pipelines in Python
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in programming, ideally with experience in languages commonly used in computational biology (e.g. Python, C). Experience with HPC, workflow managers (e.g. Snakemake, Nextflow), and containerization
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning