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profound knowledge in computational and theoretical physics/chemistry. Capability of team work is essential. Skills in high-performance computing, materials chemistry, theoretical chemistry, molecular
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/Diploma) in the field of Biochemistry, Biology, Pharmacy or Chemistry or related field Experience in experimental laboratory work Excellent knowledge of a broad range of biochemical, cell and molecular as
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and the conditions under which desorption may occur. Little is known about how environmental ageing and transport affect the sorption behavior of plastic particles. In arable soils, key knowledge gaps
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Description In the Leibniz Institute of Plant Biochemistry, the research group Symbiosis Signalling invites applications for a PhD position in biology (m/f/d) (Salary group E13 TV-L, part-time 65
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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HPC environments Good communication skills to interact with collaborators ranging from machine learning researchers to pathologists or medical students Knowledge of biology and medicine is a plus Highly
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materials and manufacturing processes, especially for the transportation sector and medical technology. To this end, materials are tailored and manufacturing processes are designed to conserve resources
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Master’s degree (or equivalent) in mathematics, computer science, physics, or related field. Sound knowledge in (scientific) machine learning, and knowledge in numerical analysis and numerical linear algebra
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the field of Biotechnology/Biology/Bioprocess engineering or related field Knowledge of genetic engineering, protein production and cell-free approach as well as bioprocess engineering, bioproduction
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Completed university studies (Master/Diploma) in the field of Chemistry, Radiochemistry, Radiopharmacy, Radioecology or related field Excellent knowledge of a broad range of organic chemistry and analytical