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Field
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Applications are invited for a 2-year position in the field of CFD and modeling of heat pumps at the Department of Mechanical and Production Engineering, Aarhus University, Denmark. Expected start
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-on experience in training large neural networks (generative models). Hands-on experience in medical image reconstruction. Preferred Qualifications: Proficiency in Python, C/C++, and Unix-like operating systems
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atmosphere models to simulate observations of ensembles of transiting exoplanets and their host stars to interpret observations with the Hubble and James Webb Space Telescopes, and the NASA Pandora SmallSat
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Excellent writing skills for article and grant writing Excellent presentation skills System dynamics modeling and simulation (e.g., Stella, Vensim) Duration: One year with expectation of renewal for 2 years
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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and/or flow cytometry-based readouts of infection Experience with biochemical assays (e.g. Western blotting) Experience handling/processing samples in vivo infection models and/or human samples
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-Neptunian objects for the various suggested theoretical models. The developed workflow should also be applicable to the other discovery science projects in Dynaverse. Your tasks in detail: Perform scientific
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to consider the Ericksen—Leslie model with stochastic fluctuations and investigate it in terms of existence of solutions and their approximation, the associated large deviation principle and the simulation
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casualty care. These projects include closed loop physiological controller development, creating in silico computer models for mimicking human physiology in trauma, and automating ultrasound image