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for use in modelling and analysis. Running model simulations (training will be provided). Analysing simulation outputs and presenting results through data visualization. Qualifications We are looking for a
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series. Apply tissue imaging methods to visualize in vivo gene expression patterns in specific tissues. Work with non-model organism data and tissue. Collaborate with a multidisciplinary team to synthesize
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analysis techniques, including space syntax, isovist measures, and visual complexity assessments. The successful candidate will work closely with researchers at Cambridge and ETH Zurich to quantify spatial
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the research, including stimulus creation (i.e. a database of visual, haptic and auditory stimuli for experiments), data collection and preliminary data analyses. The work will be conducted under
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machine learning, deep learning, data visualization, and applied analytics for multi-modal datasets. Technical proficiency with Python, R, SQL, SPSS, Tableau. Architectural and design software expertise
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and with expertise spanning knowledge graphs, GeoAI, research data management, geo-semantics, geographic information retrieval, geo-visualization, social sensing, and related fields. We are interested
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interpretation. A Ph.D. in biostatistics, statistics, bioinformatics, computational biology, or computer science is required. Strong computational background with programming and visualization skills in R and
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: responsive to objects and to interlocutors, aligning speech and visual signals for richer, more natural interaction. In this project, you will carry out research as described in the project proposal, which
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evolution experiments. You have experience handling and taking care of live fish. You have demonstrated research experience in the field of evolutionary biology or visual ecology. You are ambitious and
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, the biological light-sensitive pigment, is the actual magneto-receptor. Visual opsins, also named rhodopsins, are transmembrane proteins arranged in membrane stacks within photoreceptor cells in the eye