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Field
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work environment. We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis and modeling. The successful candidate will investigate new algorithms
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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machine learning, modeling algorithms, and/or mapping applications. Applicants must have a PhD in ecology, wildlife sciences or geospatial modeling. Additional Information: Salary Information: Commensurate
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management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding
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architectures, frameworks, and prototypes for high-fidelity visual simulations and AI-enabled environments and applications. Apply computational models, data-driven algorithms, and physics-based simulation
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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algorithms for image analysis to facilitate and optimize early disease screening, diagnosis, and treatment. Ideal candidates should possess a M.S. or PhD (or equivalent) in computer science, mathematics
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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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, correlation analysis) ⢠Integrate and benchmark generative-modeling approaches ⢠Design and implement XAI algorithms ⢠Collaborate with clinicians to validate interpretability outputs Software Development
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical