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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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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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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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experiments, etc. Perform data collection and analysis Test, optimize, troubleshoot new and existing methods and techniques, and update protocols Prepare and maintain biological stocks Maintain proper lab and
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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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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. Ability to use computer algebra software (e.g. Mathematica or Maple) for symbolic computations 2. Good numerical skills 3. Experience in field-theoretical methods (e.g. supersymmetry) 4
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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
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Experience developing pipelines and code for gravitational-wave searches and/or parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing