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or analytical many-body techniques. Requirements: very good university degree in physics or a related area interest in problems of theoretical condensed matter willingness to collaborate with experimental and
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completed bachelor's AND master’s degree in chemistry with a focus on analytical or environmental chemistry from a university or comparable institution of higher education is required. Applicants must have a
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of inorganic ions and organic matter can cause defects in electrolyser stacks, resulting in costly process disruptions. This project considers: i) development of analytical methods for the quantification
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combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics, stochastic dynamics, Monte Carlo and analytical methods) and its thorough
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(Python, Julia, C++, …) Good analytical skills Good organizational skills and ability to work both independently and collaboratively Effective communication skills and an interest in contributing
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Master’s degree (or equivalent) in a relevant discipline such as computer science, mathematics, physics, or data science. They should have strong analytical skills related to statistics, machine learning
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combines state-of-the-art computational multiscale modelling (using DFT/TDDFT methods, collision theory, molecular dynamics, stochastic dynamics, Monte Carlo and analytical methods) and its thorough
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to work independently and in a structured manner Strong analytical thinking and problem-solving skills Willingness to collaborate in interdisciplinary research teams Motivation to engage in scientific
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finish their MSc in a relevant discipline, such as computational neuroscience, cognitive (neuro-)science, or psychology. The ideal candidate is self-motivated, comfortable with both analytic and critical
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, engineering, or a related field Proficiency in at least one programming language (Python, R, C++, Julia, …) Good analytical skills with a sound understanding of data evaluation Prior experience with single-cell