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convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya, S. & Pandey, M. (2024). PCFRIMDS: Smart Next-Generation Approach for Precision Crop and Fertilizer
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, imaging). • Solid foundations in signal processing and statistics. • Experience with machine learning for regression (e.g., tree-based methods, neural networks) • Hands-on experimental skills: ability and
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Job Purpose To make a contribution to an ERC-funded project Dynamic network reconstruction of human perceptual and reward learning via multimodal data fusion, working with Prof. Marios Philiastides
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The position will involve developing new mathematical/computational methods at the intersection of scientific computing and machine learning. At a high level, the project is to build neural network models
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Previous Job Job Title Post-Doctoral Associate - Measurement and Manipulation of Developing Cortical Networks Next Job Apply for Job Job ID 370353 Location Twin Cities Job Family Academic Full/Part
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
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, clustering, neural networks) for spectral interpretation, segmentation, and material identification. • Create and curate a dedicated HSI spectral library for cultural heritage materials, linking reference
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for action ("affordances") shape neural representations, perception, and behavior. Why this position? You will sit at the center of a uniquely cross‑disciplinary team and work closely a network of
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: The candidate must have a PhD and extensive experience in modern deep neural network-based techniques. The ideal candidate should have: A PhD and a strong record of research or applied work in deep learning, with
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neural networks that handle the many challenges of integrating such complex medical data sources on large-scale studies and the translation to clinical practice. Qualifications PhD in (Bio-)Statistics