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Legchenko's research group. The project focuses on the modulation of fibroblast differentiation in the context of right ventricular pressure overload, both with and without pulmonary arterial hypertension (PAH
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interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Control Theory Formal Methods Machine Learning Context The applicant will be directly advised by Prof
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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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interpretation of OMICs datasets (DNA, RNA, proteome) Supervision and technical guidance of Bachelor’s, Master’s, and PhD students, as well as technical staff within the project context Participation in teaching
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spoken Good knowledge of German Good knowledge of Spanish or Portuguese Also welcome are: Regional research connections to Latin America Teaching experience in a university context Prior experience in
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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to drumming, sonic healing (e.g., in New Spiritualities and SBNR contexts), technologies of sound reproduction and amplification, and underrepresented areas (e.g., Tibetan and Himalayan region, mainland
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courage to contribute both to the development and application of new methods to investigate the dynamics and consequences of hypoxia in vivo in the context of different pathologies Excellent oral and
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
states, particularly concerning the balance between conservation priorities and economic interests. In this challenging context, the scientific community must continue to generate robust data and