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Field
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successful candidate will have previous experience in computer science or data science, with a PhD and publications in at least one of the following areas: Formal modelling and verification of business
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conducting both quantitative and qualitative research, including data collection and analysis Proven ability to obtain ethics and research governance approvals for human subjects research Track record
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: PhD in solar energy, electrical engineering, or environmental sciences. Proficiency in PV systems, instrumentation, and performance measurement. Experience in processing environmental data (Python, R
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mental health Knowledge of qualitative, participatory, and creative research methods Experience in analysing qualitative data using thematic analysis and analysis software (e.g., NVivo) Demonstrated
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data collection and scenario design. Techno-Economic & Environmental Analysis Compare material intralogistics handling alternatives (IPCC, trolley assist, battery trucks, etc.) via life-cycle cost and
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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arrangements. Requirements The ideal candidate will have a PhD in Economics, Innovation Studies, Scientometrics, Data Science, or related quantitative social sciences. They will have curiosity to explore and
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-funded project TOADAPT, which investigates the social-ecological adaptive capacity of forests across multiple scales and disturbance regimes. Your profile Completed PhD in forest ecology, environmental
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DPhil students, manage data analysis pipelines, and contribute to publications and grant writing. This post is ideally suited to someone aiming to secure a long-term fellowship and build an independent
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analysis of data including measures of pupil dilation, microsaccades, and behavioral measures of speech perception. Experience with data collection and statistical modeling of time-series data are essential