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. With cutting-edge research, top-tier education, and extensive collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational
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grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The position is part of an on-going
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collaborations, we are a key force in the field. Our core competencies include in vitro ADME models, advanced in vivo methods, computational modeling and simulation, as well as patient-focused and policy-related
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factor in the design of the project. The project and research environment will give the dedicated student the opportunity to train as a first-class researcher. A person who is employed as a PhD student
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for one to two PhD students in analytical chemistry to develop analytical methods for single cell analysis and mass spectrometry imaging using direct infusion mass spectrometry. The PhD candidate will work
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methods for single cell analysis and mass spectrometry imaging using direct infusion mass spectrometry. The PhD candidate will work with and develop custom made techniques coupled to high resolving mass
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networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit
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applying methods such as genome editing, live-cell imaging, and/or biochemical assays will be valuable, as the project involves perturbing protein localization in human adipocytes and assessing downstream
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molecular simulations. Previous hands-on experience in more than one of the following methods is considered an advantage: molecular simulations, Python programming, machine learning, or quantitative analysis
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of research methods in applied or agricultural and food economics, as well as strong quantitative and statistical skills. Proficiency in English and excellent communication abilities are required. Motivation