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observational and modelling approaches to ice-shelf processes The doctoral student is expected to: Develop research questions within the thematic scope of Antarctic ice shelf interactions Analyse observational
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skills in Matlab and/or Python, R C2 Experience with collection and analysis of EEG and/or fMRI data C3 Experience with Drift Diffusion and/or Reinforcement Learning models C4. Research creativity and
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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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potential in preclinical experimental models. Requirements: Applicants must have obtained their PhD within the last five years and have experience in the study of non-conventional lymphocyte populations, as
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related to models and multiple sources of data describing ecological dynamics. The PhD project will address the following aims: 1) Develop efficient tools for learning about models from data, 2
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NIST only participates in the February and August reviews. Computer-based tools, including the NIST Alternatives for Resilient Communities model, or NIST ARC, are being developed to support
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, and Azure—and designs future-state structures that support data products, analytics, automation, and AI/ML enablement. Establishes enterprise data standards, models, governance structures, and
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). The proposal lies at the intersection of digital twins, AI techniques, and predictive model development, proposing an integrated and scalable ecosystem capable of enabling new energy management
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, progression, and treatment outcomes. Skills in applying causal inference, survival analysis, and longitudinal modelling to link clinical and biological data. Expertise in predictive modelling and AI
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. This postdoctoral position at KTH focuses on developing advanced modelling frameworks and techno-economic analyses within two national research projects addressing virtual energy sharing and large-scale