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courses). - Proficiency with computer tools: R, Python, Bash, Perl, Java, SQL. - English: High-level language proficiency. - Willingness, ability to learn, and teamwork skills will be valued. - Experience
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experience and a background in quantitative finance, statistics, data science, machine learning, or artificial intelligence. This is a Haas-funded position. Key Academic Responsibilities: Advising: Serve as a
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-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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Aerospace can be found at: https://www.issaerospace.com/ . Qualification / skills we require: A good degree in Aerospace, Mechanical, Electrical Engineering or a closely related field. Strong technical
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | 22 days ago
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about Artificial Intelligence, Machine Learning
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/computer engineering, applied mathematics, computational biology, bioengineering, or a closely related discipline. Solid experience training and evaluating AI models. Proficiency in Python and ML frameworks
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and