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learning frameworks such as PyTorch or TensorFlow is an advantage); Affinity with probabilistic modelling and spatial data analysis; Interest in the physical behaviour of water systems and hydrology (prior
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performance simulation, lifecycle assessment, or climate resilience. Knowledge of uncertainty quantification and risk modelling is desirable. You have experience with programming and data analysis (e.g., Python
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programming skills (Python; familiarity with deep learning frameworks such as PyTorch or TensorFlow is an advantage); Affinity with probabilistic modelling and spatial data analysis; Interest in the physical
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. We value personal development: you will receive training in advanced spectroscopy, data analysis (e.g., Python or MATLAB), and scientific communication. You’ll have the opportunity to attend
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approaches. Collecting and integrating non-radiological (clinical) data for comprehensive analysis. Optimizing imaging protocols and scanner workflow. Contributing to ethics applications and study preparation
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PhD in Optical Characterization and Photonic Performance Analysis of Liquid Crystal Polymer Coatings
advantage. The ideal candidate enjoys precise experimental work, quantitative data analysis, and collaboration across materials science, optics, and device engineering. Ability to work in an interdisciplinary
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19 Aug 2026 Job Information Organisation/Company Royal Netherlands Academy of Arts and Sciences (KNAW) Research Field Environmental science » Ecology Environmental science » Global change Researcher
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closely related discipline). You have a strong interest in AI/machine learning, data mining, regression analysis, responsible AI, causal inference, and programming (R/Python, SQL). Moreover, you are driven
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experiments, and with data analysis. You also possess: A successfully completed MSc degree in environmental sciences, water technology, biotechnology, chemical engineering, biobased sciences or a closely
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using R and/or Python, with experience in data analysis, computational workflows (e.g. Snakemake, Nextflow), and relevant computational libraries (e.g., NumPy, pandas, scikit-learn, TensorFlow/PyTorch