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Field
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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visual and auditory cortices using techniques such as cross-modal decoding, unit reliability analysis, and shared variance component analysis (SVCA) Create comprehensive data visualisations and perform
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to historians, educators, and the wider community. The work will involve: Digitisation and computational processing of historical records, including metadata creation, textual analysis, data mining, and
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Screening Program, including analysis of large data sets of screening interpretation outcomes and of survey results, as well as presenting and writing papers on the study results. Where to apply Website https
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behavioral datasets Proficiency in scientific programming (e.g. Python, MATLAB, or R) for data analysis and visualization A clear affinity for insect behavior, sensory ecology, or movement ecology; Fieldwork
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to dedicated individuals who want to deepen their understanding of the research field: Electrical Engineering, Electronics, Information and Communication Technologies Systems Analysis, Modeling and Design
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(Python, MATLAB) for behavioral data analysis Preferred Qualifications: Experience with rodent behavior assays Familiarity with tools like DeepLabCut, MoSeq, or Kinect-based systems Interest
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scientific programming (e.g. Python, MATLAB, or R) for data analysis and visualization A clear affinity for insect behavior, sensory ecology, or movement ecology; Fieldwork experience or a strong interest in
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behavioral neuroscience, development, or ASD Programming experience (Python, MATLAB) for behavioral data analysis Preferred Qualifications: Experience with rodent behavior assays Familiarity
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systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and model development. Ability to work