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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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, or related Experience and skills · Multi- and hyperspectral images processing · Knowledge of quantitative remote sensing · Knowledge of physical and statistical modelling concepts
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to a PhD position in our team Copies of diplomas and academic transcripts Contact information of at least 2 referees Early application is highly encouraged, as the applications will be processed upon
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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advanced computational analysis of large-scale neural recordings. What you would be doing: Process and analyse large-scale calcium imaging datasets from multisensory experiments, including neural responses
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focus on research and development in Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization (AP-MALDI) mass spectrometry and molecular imaging, using high-resolution Orbitrap MS instrumentation
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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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complete picture of fish habitat use and connectivity. The PhD is part of the section for Ecosystem based Marine Management and the Marine Habitats research group, as well as several synergistic initiatives
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing