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Program
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imaging, both in vitro and in vivo. Develop, optimize, and maintain image analysis scripts and tools using Matlab/Python/LLMs (segmentation, quantification, post-processing), in line with the needs
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the analysis and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools (LTspice, Cadence, MATLAB, Python) Excellent communication skills and ability to work in a team are
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for a postdoctoral associate position, ideal candidates will have experience and prior training in some of the following areas: Programming in Python and/or Matlab. Electrophysiology, calcium imaging
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PhD student will expect to develop some experience in developing power systems models using a range of computer languages and tools (e.g. Python, MATLAB, OPNET, etc), ideally for applications involving
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understanding of advanced programming areas, including modern machine learning and deep learning methods (e.g., MATLAB and Python) Expertise in one or more of the following areas is preferred: mechanical
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) systems. Proficiency in MATLAB, Python, and LabVIEW programming languages is essential. Demonstrated research experience with a strong record of peer-reviewed publications in fluid dynamics, turbulence, air
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languages (e.g. Python, C++, Java, Fortran or Matlab); has knowledge of advanced GNSS data processing and analysis methods, including visualization; has a scientific publication record in journals indexed in
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), Experimentation, Genome-Wide Association Study (GWAS), Laboratory Operations, Laboratory Techniques, Magnetic Resonance, MATLAB, MRI Analysis, MRI Software, Neuroimaging, PET/CT, Presentations, Python (Programming
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control (broadly construed). Expert knowledge of a scientific programming language (preferable: MATLAB or Python) Experience with one or several of the listed human neuroscience techniques. Ability to work
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scientific programming and algorithms is desirable Experience working in a research environment a plus General languages (preferred): C++, Python, MATLAB CUDA (strongly preferred) Machine learning knowledge