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Field
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learning and deep learning techniques to the biological sciences. The ideal candidate will have expertise in artificial intelligence, with a specific focus on deep learning applications in structural biology
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as natural experiments and interpretable machine-/deep-learning models. Publish research results in high-quality international journals (at least two peer-reviewed papers are expected). Eligibility
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-career scientist to develop cutting-edge machine learning approaches for understanding and designing pathogen antigens. This is a unique opportunity to help shape a new research program at the intersection
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with
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Qualifications Experience with machine learning and deep learning Ability to analyze and interpret complex geophysical data and apply appropriate research methodologies Additional Information: The College of Arts
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, biomedical engineering, medical imaging, or related field. Experience in deep learning with practical implementation. Strong Python skills and relevant frameworks. Experience with large clinical imaging
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. deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | about 2 months ago
across European life-science AI efforts. Requirements PhD in computational biology, bioinformatics, machine learning, or a related computational field Hands-on experience with foundation models / large
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical