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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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ecosystems. Fairness, safety, interpretability and trust in AI algorithms and AI-based technologies. Applications with healthcare, energy, sustainability, business analytics, etc. Candidates are expected
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and algorithms to analyze complex data from X-ray, electron, and neutron experiments. This role involves research and development in combining advances in Fourier analysis, optimization, machine
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need. Apply AI and machine learning algorithms to software engineering projects in the researcher’s specific domain. Application of Domain Expertise Initiate and maintain open collaboration with
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drug experts (clinicians, clinician scientists, data scientists, and laboratory investigators) to co-develop phenotyping algorithms but is expected to serve as the domain expert in high-throughput
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updates to address system changes, including new encryption algorithms, authorization protocols, data packet configurations, and additional user groups. Maintains and upgrades software and hardware
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: Python, R, Java, C++, C#.Net, SQL. Proficiency in using Business Intelligence tools (e.g. Power BI, Tableau or Qlik Sense) Experience in applying machine learning techniques and designing algorithms
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neuroscience, physics, or related fields is required. Minimum 1-3 years of experience in Monte Carlo simulations. Strong programming skills in Python. Proficiency with Monte Carlo algorithms, reaction-diffusion
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insight from this data. * Researches and monitors industry and competitor brand activity, trends, key platform/algorithm changes, influencers, and identify emerging trends. * Works cross-functionally and