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of cell populations from omics data Performing phenotypic assays to validate the cellular heterogeneity The analysis of generated data and communication within an interdisciplinary team made up of people
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to collaborative project across teams within our Center, as well as joined initiatives with external academic and industrial partners Data analysis, interpretation, visualization and integration Keeping an industry
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
analysis of parameterized finite‑element models. By integrating data‑driven and FEM‑based approaches, a hybrid model is developed that accurately represents physical relationships while providing real‑time
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Research Assistant to investigate digital reading comprehension through large-scale data analysis. This role, funded by the John Fell Fund, sits at the fascinating intersection of computational
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multidisciplinary supervisory team spanning engineering, ecology, and environmental science, with access to state-of-the-art laboratories, field sensors, and data-analysis facilities. You will also join Manchester
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their own vehicle and find their own housing for the duration of the position. QUALIFICATIONS Knowledge of computer programming language(s) for data analysis and visualization Knowledge of spreadsheet
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and optimize methods to measure neuronal activity and morphology. Combine the two methods to detect the cell type and neuronal physiology in one experiment. Computational & Data Analysis Apply advanced
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for data acquisition and analysis (e.g., Python or LabVIEW) are highly desirable. After a training period, you will operate and maintain the fast EC-STM system and contribute your own ideas to the project
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involved in the analysis of sleep and neuroimaging data, interpretation of the results, scientific paper writing, and presentation of the findings at national and international conferences. The successful
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the JR166 data set. This includes advanced gamma-ray spectroscopy analysis, extraction of transition strengths, and systematic comparison with state-of-the-art theoretical models. The project offers