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) , Lattice QCD , Machine Learning , Neutrino Astronomy , Neutrino physics , Nuclear and Many-Body Theory , Nuclear Theory (nucl-th) , Particle Astrophysics , Quantum Field Theory Appl Deadline: 2026/01/02 04
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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your application: A doctoral degree in automatic control, electrical engineering, computational materials science or related. Research experience in battery tests, machine learning, data-driven
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combinations of theoretical physics, mathematics, and machine learning. While machine experience is not a strict requirement for the job, we anticipate that the strongest candidates will have clear research