37 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Leibniz
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, Environmental Sciences, Computer Sciences Proven experience in financial economics, financial risk assessment as well and in climate and ESG ris Interest to work at the interface between research and policy, and
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populations. This project will explore the genetic and evolutionary mechanisms shaping adaptation through a combination of genomic, computational, laboratory, and field-based approaches. Research focus
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phenotyping, including image analysis evaluations, for trait quantification Handle NGS datasets for RNAseq or SNP detection and linkage analysis using R Your qualifications and skills: You have a PhD or
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collaboration and mutual learning access to high-performance computing a chance to contribute meaningfully to an ambitious research agenda focused on creating positive impacts for global society and future
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assistants Your profile: PhD in social, I/O, or experimental psychology experienced in experimental research Interest in designing online interventions Profound knowledge of English Please contact Prof. Dr
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Partnership Your qualifications: PhD in environmental / agricultural science, (rural) sociology, human geography, political sciences, or related subjects Knowledge of transdisciplinary approaches and methods in
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profile Completed PhD degree in mathematical modeling preferably in the field of evolutionary ecology. Experience with developing mathematical models preferably in the field of evolutionary ecology (e.g
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Mentoring graduate students Your profile: PhD in Ecology or a related field Solid background in (macro-) ecology and biodiversity research. Solid background in zoology preferred but not essential. Advanced
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, aggregation, linking and retrieval of comprehensive heterogeneous and distributed data sources. To this end, both statistical and linguistic analysis methods (NLP) as well as machine learning in combination
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-processing, and machine learning textual analysis of the full text of policy documents. Qualitative content thematic analysis is envisioned to compliment structural topic modelling to identify strategies and