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(Wissenschaftszeitvertragsgesetz - WissZeitVG). The position aims at obtaining further academic qualification (usually PhD). Professional assignment: Chair of Knowledge-Aware Artificial Intelligence (Prof. Dr. Simon Razniewski
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Python • Ability and enthusiasm to learn new technologies quickly • Ability to work highly motivated both independently and in a team • Very good written and spoken English skills • Some knowledge or
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academically. If you fully meet all of the following mandatory requirements, we look forward to receiving your application: very good knowledge of materials science, particularly of metallic materials documented
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Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl
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: Excellent study or research performance You possess high scientific integrity Master’s degree (or equivalent) in immunology, biology, biomedicine, microbiology, bioinformatics or similar Knowledge in
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very good results Knowledge in the fields of environmental economics, resource economics, economic theory, evolutionary economics or innovation economics are an asset You are enthusiastic about complex
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. Profile: • As a successful candidate (m/f/div) you should have a Master’s degree or Diploma in Chemistry, Materials Science or Chemical Engineering. • Profound knowledge of materials synthesis and their
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spectrometry of radionuclides, knowledge in software development for data acquisition, experiment control, and data analysis are desirable. We offer a fixed-term 3-year PhD position with the salary and benefits
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culture and cell manipulation Fluency in English is required. Knowledge of German is not required Our Offer: Scientifically stimulating environment with excellent facilities and vibrant chromatin community
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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph