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: To work within an interdisciplinary environment with leading experts in seismo-acoustics, High-Performance Computing (HPC) simulations, and real data. A net monthly salary (including basic health insurance
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network optimization for 6G/FutureG wireless networks especially on the Internet of Intelligent Things under the supervision of Dr. Lingjia Liu (https://lingjialiu.ece.vt.edu). Successful candidates will
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, promoting your research, networking, etc.). Strong verbal and written Communication Skills. Position Details: The Department of Cell and Molecular Physiology is an open, collaborative, and stimulating
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Appointment Dates: July 1, 2026, to June 30, 2027 Proposed Compensation: $68,500 or commensurate with experience. Email/Website: [email protected] , https://www.reilly-lab.com(Link is external) , https
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at: www.ftf.lth.se , www.nano.lu.se , https://kaw.wallenberg.org/en/research/semiconductor-bandgap-key-future-green-electronics, https://c3nit.se/ Subject description The purpose of this project is to develop a data
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one of the following areas: - Methodology development in wavefunction-based electronic structure methods, quantum Monte Carlo, tensor networks, or quantum embedding methods
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University of California, Los Angeles | Los Angeles, California | United States | about 23 hours ago
forests, gradient-boosting methods, PLS-based approaches, neural networks, or related methods is preferred. Experience with feature selection, model interpretation, cross-validation, external validation
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | about 2 months ago
-associated regulatory networks, and ultimately influences gene expression. To address these questions, the project integrates molecular biology, mammalian cell biology, quantitative fluorescence microscopy
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are The Department of Biology (http://bio.au.dk/) provides a framework for research and teaching in all major biological subdisciplines. The department is especially known for its research contribution in biodiversity
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-learning architectures for sequential data (e.g., Transformers, graph neural networks, state-space models). Experience with OpenCV, GPU-accelerated inference, Docker, and modern software engineering