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Field
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requirements, including link budgets, beam steering, and orbital pointing dynamics. • Experience with optimization methods and physics-informed machine learning. • A strong publication record in antennas
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electroluminescence and photoluminescence imaging, preferably daylight and field-based methods. Proven skills in data analysis, image processing and machine learning. Experience with PV performance modelling, power
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Mathematics, Computer Vision, or Data Science. -Knowledge of statistical inference methods and machine learning. -Experience in spectroscopy and imaging is an asset. -Strong programming skills in Python
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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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artificial intelligence with a preferred focus on computer vision and medical image analysis. Preferred Qualifications: PhD in Medical Physics, Bioengineering, Biomedical Engineering, Physics, Computer Science
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Experience with super-resolution ultrasound, US localization microscopy, photoacoustic imaging, elasticity imaging, pulse encoding, solving inverse problems, machine learning, AI, SolidWorks, 3D printing FLSA
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imaging (MRI). The position is within the Research Council of Finland (RCF) consortium project focusing on the development of low-field MRI hardware, sequences, image reconstruction and applications, in
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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systems, with a focus on 3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation