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companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus
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, Psychology, Neuroscience, Computer Science, or a related field, interested in interdisciplinary approaches to understanding human cognition, experience with experimental research and data analysis, and
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valuation of 30%. a) Curricular Evaluation Criterion a1. Absolute merit of the candidate (30%). Considering his/her educational background and research experience. Criterion a2. Knowledge of tools and
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via chromatographic techniques; • Research Area 3: Adsorption study of organic molecules on various materials. Candidate profil: General, theoretical, or disciplinary knowledge: - Materials Chemistry
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will possess sufficient specialist knowledge in cognitive neuroscience, developmental science, reading/language, visual attention, neuroimaging or electrophysiology to work within the established
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multidisciplinary environment within WFBR, working closely with experts in food technology, microbiology and chemistry. Knowledge sharing is central to how we work: we meet several times a week in Wageningen to
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chemistry. Experience with regional or global modeling is desirable. Strong programming skills are necessary. Good knowledge of English (spoken and written) is essential. Employment will be according
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the industrial production, as the latter requires knowledge of high-temperature thermophysical properties of materials as well as an understanding of physico-chemical processes at the free surface, etc. However
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Sylt Ost, Schleswig Holstein | Germany | 12 days ago
interdisciplinary marine and groundwater research Knowledge of hydrogeology, submarine groundwater discharge, coastal groundwater systems or groundwater geochemistry Experience with fieldwork and/or geochemical
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, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple-instance learning or high-performance computing is an advantage. Prior knowledge of pathology or breast