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Earth Engine, ENVI, MATLAB, or R. Desirable Proficiency in applying machine learning methods to multispectral and hyperspectral data for detecting crop diseases and estimating crop yield and quality
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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an active role in data management, processing, and quantitative analysis (e.g., longitudinal and multilevel modelling, time-series or high-frequency data analysis, machine learning or predictive
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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and spectral index time series extracted you will work with solar physicist Mykola Gordovskyy (also at CAR) on initial physical modelling to better understand these extreme flares and to compare them
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mapping tools such as Google Earth Engine or Microsoft Planetary Computer, and it is likely that developments such as satellite data embeddings and GeoAI will offer potential for increasing the accuracy and
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Innovations Group seeks a forward‑thinking expert in statistical machine learning to translate complex biological datasets into actionable AI‑driven insights. You will enhance genomic selection and breeding
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. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning