38 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Leibniz
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or Python Machine learning methods (for the baseline prediction for the reward funds) is beneficial We expect: Strong motivation to contribute to policy-relevant research Strong interest in teamwork and
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within a joint research programme. What will be your tasks? The position focuses on the co-design and co-development of a monitoring assessment and policy framework for mitigating changes in marine
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Your Profile Doctorate (PhD) in Ecology, Soil Ecology, or a closely related discipline Expertise in identifying soil invertebrates Experience with the analysis of food webs or trophic interactions
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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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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