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Field
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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computing algorithm to implement the modelling of inelastic molecular collisions on present-day quantum computing hardware (e.g., IBM-Q, IonQ) within a framework of mixed quantum/classical theory
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for diagnosis and prognosis of different sarcomas. He/she will develop and train deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research
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responsibilities may include improvements to the geodetic early warning algorithm, G-FAST, and testing scenario earthquakes in different regions around the world. Minimum Education Required A Ph.D. is required, with
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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designing and benchmarking integration algorithms. The position is situated in the Intelligent Data Engineering Lab and will be supervised by Dr. Jan-Christoph Kalo and Prof. Paul Groth. What you will do
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classical computing algorithms are NP-hard or, in general, difficult to implement. Within your application, please provide a research proposal (no more than five pages) answering the following questions: What
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knowledge of C++ in relation to scientific software. You have acquired one of the two following skill sets (both is a strong merit): Previously conducted implementations of algorithms and simulations using
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods