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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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in wireless communications and networking Background in AI and machine learning is an advantage. Experience and skills Knowledge of random-access protocols (e.g. IEEE 802.11 family). Understanding
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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
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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
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wireless communications, RF signal processing, and/or applied machine learning Strong background in digital communications and RF signal processing, ideally with experience in SATCOM, NTNs, or space-borne
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
Qualifications PhD in machine learning, computer vision or a related field. Established expertise in deep learning methods applied to images analysis. Experiences with generative models, volumetric image
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of the following methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and