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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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experience in managing plant growth facilities, in cultivating plants, microscopy, experimental design, statistical analysis and have knowledge on high-throughput image analysis, and hold a valid driver's
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. You have a solid background in computer science, mathematics, or statistics and a strong interest in empirical analysis and interpretation. Areas of specialization can be, for instance, network analysis
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at international conferences. Your profile Required qualifications include a Master's degree in computational biology or a related field. Prior experience with programming, statistics and biomedical research is
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requires a strong background in statistics and experience implementing analyses using scripting languages (e.g., R and Python), as well as experience working with large, complex, and unstructured datasets
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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 2 months ago
Environmental science » Other Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Country Switzerland Application Deadline 9 Mar 2026 - 22:59 (UTC) Type of Contract Permanent Job Status Full
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cleaning and processing of data, visualization and preliminary statistical analyses In addition, you might support the team in smaller tasks, such as the annotation of texts, literature searches, and more
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to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high-throughput metabolomics, time-lapse
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statistical modelling; familiarity with or interest in machine‑learning approaches is an advantage, but not a requirement. Excellent written and spoken English communication skills. A genuine interest in
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Models, Reinforcement Learning, Robotics, Safety, Security and Privacy, Scientific Machine Learning, Smart Materials, Social Questions, Soft Robotics, Statistical Learning Theory, Visual Analytics