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perform both empirical and theoretical work. You will learn how to collect and analyse data within your research area as well as communicate your results at national and international conferences and in
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, Multi-omics, or Bioinformatics A strong track record of high-quality research, demonstrated by publications and successful grant applications At least three years of postdoctoral research experience is
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. We are seeking passionate individuals who are committed to making a meaningful impact through collaboration, innovation, and excellence. To learn more about our mission and work, please visit https
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are looking forward to receiving your online application. The full, legally binding call for application (in German) including the salary can be found here: https://lfuonline.uibk.ac.at/public
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 10 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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how AI is changing work. Its projects examine what happens when generative and agentic AI enters leadership, everyday workflows, team communication, professional learning and career decisions. Across
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organizational theory, the learning sciences, digital transformation, digital technologies, human-computer interaction, and related fields. Within the specific field, the PhD student will engage in both research
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, including researchers, faculty members, engineers and technicians, as well as non-permanent staff, including fixed-term contract researchers, postdoctoral researchers, PhD students and interns, distributed
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imaging. Experience with scientific programming (e.g., MATLAB, Python and/or C++). Excellent analytical and problem-solving skills. Interest in image reconstruction, beamforming, machine learning, and
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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant