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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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of the following areas: Image processing, partialdifferential equations, scientific computing, deep learning.•Solid programing skills. Knowledge of Python, Tensorflow/Pytorch, and Mathematica.•Fluent
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a team More specifically: - For mission 1: knowledge of signal and image processing, machine learning (PyTorch or TensorFlow + NumPy/SciPy), statistical processing & data and results visualisation
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motility apparatus through single molecule fluorescence imaging approaches. Underpinning genetic and biochemical experiments will also be required. The project will build on our recent work in this area
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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engineering, and quantitative live-cell imaging to probe and model these processes. By combining stem cell biology with cutting-edge microscopy and physical concepts, we aim to establish a predictive framework
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experience in computational methods, which may include machine learning. This could involve medical image analyses and processing or biomarker quantification approaches. Experimental set up experience would be
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. Strong background in neuroimaging techniques (e.g., MRI, PET) and computational modeling. Proficiency in programming languages such as Python, MATLAB, or R. Experience with image processing tools and
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the supervision of Dr. Merry Mani. The successful candidate will work on the development, validation, and translation of cutting-edge MRI techniques for imaging slow-flowing neurofluids and brain microstructure in
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property analysis, sorting, and grading. Contribute to the TIMBERHAUS project by applying advanced image processing and AI methods for timber quality assessment. Integrate experimental data with