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, ROS) Solid skills in numerical analysis Advanced knowledge of computer vision Experience in human–robot interaction Particularly Meritorious It is particularly meritorious if the applicant has: A PhD
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. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve computational biology/chemistry, machine-learning for biological or chemical data, metabolism, and
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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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: Computer programming skills Your Benefits At Helmholtz-Zentrum Berlin, you will benefit from state-of-the-art research facilities, and a wide range of benefits to support your scientific career: Work as part
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Qualifications The following qualifications and experience will be considered an advantage: Experience with crop modeling. Experience with plant breeding. Background in data science, machine learning, and
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should have the following qualifications: Ph. D. degree in data science, electrical engineering, computer engineering, computer science, mathematical engineering, or similar. Proven track record in
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols
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://sandrasiby.github.io/ . The lab website can be found at https://sites.google.com/nyu.edu/haven-lab The successful candidate will engage in research that includes applied machine learning, systems building, and Internet
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computing and/or cloud computing; familiarity with Earth system models through model development, model execution, and/or model performance diagnoses; applied mathematics methods such as machine learning
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Helmholtz Association of German Research Centres | Oldenburg Oldenburg, Niedersachsen | Germany | 3 months ago
research. The project will combine established methods from historical research with modern approaches from machine learning and network science. Your TasksThe work will involve the analysis of historical