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and generative methods, with the goal of delivering impactful research outcomes and publications at leading venues. You will work closely with Dr. Aonghus Lawlor (PI), alongside other postdoctoral
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vision, animal welfare, and behavior. The position is aimed at a method-oriented researcher interested in developing and applying computer vision–based methods in real-world settings. The project is part
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, or reading groups), present their work at research methods and field-specific seminars, assist with DDSS projects on occasion, and offer consultation to faculty, graduate students, and postdoctoral researchers
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. The candidate's work may use empirical or theoretical methods to address important policy questions. The position offers an outstanding opportunity for independent research, as well as opportunities
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first-principles multiphase flow descriptions with data-driven components Formulate and implement parameter estimation and system identification methods for multiphase flow models Integrate experimental
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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; however, the greatest interest is for applicants with interest in one or more of the following topics: Computationally intensive methods in Statistics such as Count Time Series, Curve estimation
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Computer Engineering or a closely related field, completed by the start date of the appointment. U.S. citizenship is required. Research experience in at least one of the following areas: ● Chip design
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methods, enabling precise midIR spectral measurements from blood rich skin layers. Your work will bridge measurement physics and biomarker estimation: you will transform raw optoacoustic signals into robust
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functionalization of materials, especially Pb-free Metal Halide perovskites (MHPs), including all inorganic and 2D materials, and oxides. · Fabrication of functionalized Pb-free MHPs by solution processing methods