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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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Degree Doctor of Philosophy (PhD) / Doctor rerum naturalium (Dr rer nat) Course location Hannover In cooperation with Twincore - Centre for Experimental and Clinical Infection Research, University
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, Higgs physics, particle dynamics in the early Universe, collider phenomenology, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond
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(1–4) and in related projects. We encourage potential PhD candidates to visit our webpage to learn more about the research we are conducting. The PhD candidate is expected to be enrolled in two
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of Vienna). About the position: Lead the research group focusing on hybrid quantum algorithms, quantum neuromorphic computing, and quantum machine learning Build your own team (PhD students, postdocs) 4-year
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on the research strengths in bioengineering, data analytics, artificial intelligence, and machine learning. More information on our research strengths can be found at https://www.uta.edu/academics/schools-colleges
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Economics, Mathematical and Computational Biology, Theoretical Ecology), Statistics, Machine Learning and Data Science, and Theoretical Computer Science are especially encouraged to apply. The School has
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and machine learning models. To be successful in this role, you will have excellent communication skills and written English, strong quantitative and analytical skills, the ability to work creatively
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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preferred Excellent knowledge of microeconometric methods for causal inference; knowledge of machine learning methods is preferred Experience in university teaching Strong communication and teamwork skills