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spatial data visualization tools. Building AI solutions, including chatbots, RAG, and other LLM-based tools. Interest in and experience developing software to support data-driven research. Application
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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consequences for essential ecosystem processes. In the framework of the Biodiversity Exploratories (https://www.biodiversity-exploratories.de/en/ ), funded by the German Research Foundation (DFG), the project
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Forecasting the Future of Biodiversity: Cutting-Edge Approaches to Population and Community Dynamics
: How do microbiomes vary, and what does that mean for host health and fitness? 3) Landscape-Level Change: What drives spatial variation in population dynamics across ecosystems? 4) Next-Gen Monitoring
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person with an interdisciplinary background and training. QUALIFICATIONS: REQUIRED: o Very strong statistical and analytical skills with a strong background in causal inference approaches (e.g
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learning, AI, or statistical modeling applied to biological data Experience with genomics, transcriptomics, single-cell and/or spatial omics technologies Proficiency in scientific computing frameworks Strong
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at the rank of Research Assistant Professor in applied probability, data science, machine learning, and spatial statistics. Candidates with a strong background in the development of novel models and original
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the following: Generates, compiles and presents statistical data with appropriate conclusions, recommendations, etc. pertinent to research activity. Reviews and analyzes scientific data, scientific reports
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hours; Health insurance, employee benefits, and life in a pleasant and safe city; Competitive salary. Where to apply E-mail vladimir.remes@upol.cz Website https://pracuj.upol.cz/nc/zprava/clanek/postdoc