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collaboration with national and international partners and aims to develop sustainable solutions for future materials and chemical processes. Read more about our activities: https://www.slu.se/institutioner
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programming, one in optimization, and one in machine learning at least one advanced-level course in stochastic processes, or in related subjects such as time series analysis, spatial statistics, spectral
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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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interest in working in a cross-disciplinary environment: Micro- and nanofabrication specialist – expertise in clean-room processing, lithography, etching, microfluidic integration, and planar waveguide
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of these subjects are valued: machine learning, automatic control, system identification, optimization, signal processing, filtering and smoothing, probabilistic modelling, dynamical systems
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working in a cross-disciplinary environment: Micro- and nanofabrication specialist – expertise in clean-room processing, lithography, etching, microfluidic integration, and planar waveguide fabrication
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Histological techniques, such as in situ hybridization and immunohistochemistry Confocal microscopy High-throughput screening approaches Development or optimization of molecular and experimental methods
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focus on building scalable solutions for large-scale data processing and model training. Experience in working with multimodal or vision models. Experience in working with optimization approaches. Good
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provide insights into where energy expenditure is highest and where efficiency improvements can have the greatest impact. By doing so, this work aims to contribute to a better understanding and optimization