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training opportunities for professional and personal development VBL company pension / Capital accumulation benefits Employee welfare (AWO) family service Application process Does this position appeal to you
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the funding period. Application process Applications must be submitted via the BerGSAS portal by 15 October 2025, 00:00 CEWT, quoting the reference 2026-AOViS-BerGSAS-GSSP. Late or incomplete applications will
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glycosylation, the most complex cellular enzymatic process chemical or chemoenzymatic synthesis of glycan-based reporter molecules and small molecule inhibitors contribution to scientific primary publications
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Deutsche Forschungsgemeinschaft within the Reinhart-Koselleck programme for innovative high risk-high gain research. Requirements: university degree in chemistry or physics and profound knowledge in
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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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of the areas number theory, geometry, non-commutative geometry, topology, analysis of partial differential equations, mathematical physics Language proficiency in English or German These posts are designed
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preference if they are equally suitable. Proof must be attached. Applications from people of all nationalities are welcome. Please note our information on data protection in the application process at: https
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: university and, if applicable, PhD degree (e.g. Master/Diploma) in mathematics, physics, materials science or related subjects basic knowledge of computer programming (e.g. Python, Matlab and C++) excellent
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for imaging plant tissues (rose cuttings) Realisation of a corresponding measuring stand and implementation of test series for imaging Realisation of a data processing routine for the automated detection
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measuring stand for the collection of hyperspectral data on a test field Collection of imaging data and creation of a database Establishment of a data processing routine for pre-processing the hyperspectral