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differentiation of generic and complex computer programs (including control flows, data structures, and possibly memory) allows for the exploitation of any-order differentials to obtain transformative effects
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able to further develop your skills in econometrics and quantitative as well as qualitative methods (e.g., surveys and interviews). Have a basic working knowledge of the Dutch language, because the data
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past and/or current research/activities Ability to gather and share relevant information General interest in space and space research Behavioural competencies Education You should have recently completed
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bioinformatics and data analysis (i.e. R, Python, Perl) is a significant plus. Organisation Conditions of employment We offer you in accordance with the Collective Labour Agreement for Dutch Universities (https
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researchers with diverse experimental interests and backgrounds, including spin and superconducting qubits, condensed matter device physics, quantum materials, quantum many-body physics, and quantum information
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to provide more information about the project. European and national legal instruments impose various requirements for the use of digital identity wallets. First, the eIDAS revision creates a general framework
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bulk and clonal protein expression data from large melanoma cohorts, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML
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‘RobotLab’, large numbers of experiments can be carried out, yielding large datasets on properties of molecular systems. With this data, ML models are trained to predict the properties of molecular systems
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on their expertise and tasks, fostering their professional development. RIDE supports self-reflection across five key domains of the university’s Teacher Development Model (more information: Our vision on teacher
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going to do Analyze a uniquely extensive neuroimaging dataset (90 minutes of functional MRI per participant across multiple sessions and conditions, detailed structural and diffusion-weighted data