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Field
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machine learning techniques to enhance source detection sensitivity. Multimessenger modelling and interpretation of Galactic sources. This position offers the opportunity to work at the forefront
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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to calculate your points for admission. Emphasis is also placed on your: background in algebraic or symplectic geometry or mathematical physics programming skills and experience with computer algebra packages
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Qualifications Work Experience: Experience with research involving animals. Animal Behavior training and testing. Single unit neurophysiology data acquisition and analysis. Skills: Highly motivated to learn
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independently and as part of an interdisciplinary and cross-cultural team, possess excellent organisational and communication skills, and work collegially with other staff. Advanced computer skills, including
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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brick and mortar program and a new exclusively online program) and growing BSW program. The ideal candidate will be able to teach courses in our MSW online program in both generalist and advanced
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power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English English requirements for applicants from outside
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neuroimaging and fluid biomarkers, (b) systems biology analysis of pathways from multi-omics data using multi-layered network approaches, © machine learning for identification of genetic risk factors in ADRD, (d