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Field
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gravitational effects on entangled photons for shining light onto the interface of quantum physics and gravity? Can we exploit quantum photonics technology for novel quantum machine learning, quantum computing
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proteins and pathways implicated on disease pathogenesis. We are currently analyzing brain, CSF and blood, multi-omic data (transcriptomics, proteomics and metabolomics), from a large collection of well
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deep expertise in modern machine learning and a strong record of research accomplishment who are excited to advance foundation models, agentic systems, and new AI approaches for high-impact scientific
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skills, especially in quantitative and/or mixed methods. Experience with statistical analysis including cluster analysis Comprehensive computer skills, with the ability to learn and utilize new and
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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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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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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