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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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? Then consider advancing these theories with us. In this project, we aim to extend the powerful tools of statistical mechanics on graph and network models to broader applications in soft and active
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global academic and industry network🌟 Who we’re looking for:- A curious, ambitious researcher with a strong background in signal processing, wireless systems, mathematics (statistics, linear algebra
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capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives, including large-scale
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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...) Operations Research Statistics Physics Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview As a Quantitative Research Intern at Susquehanna
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...) Operations Research Statistics Physics Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview As a Quantitative Systematic Trading Intern
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...) Operations Research Statistics Physics Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview As a Quantitative Systematic Trader at Susquehanna
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Research Statistics Physics Appl Deadline: (posted 2025/06/24, listed until 2026/06/23) Position Description: Apply Position Description Overview Susquehanna is expanding the Machine Learning group and
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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data