1 to 25 of 77 Linear Algebra Jobs

transformer architectures to create intelligent conversational agents. Dive into the world of traditional NLP techniques and stay ahead of the curve. Apply a strong understanding of fundamental concepts-statistics, linear algebra, calculus, regression, classification, and time series analysis - to extract valuable insights from data. Be the driving force behind our data visualisation efforts - whether its Tableau, Power BI … python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for optimising algorithms and models. Predictive modelling techniques for regression and classification. Time series analysis for handling time-dependant data. Deep More ❯

high-performance CUDA kernels for matrix operations and numerical solvers Profiling and optimizing GPU execution using NVIDIA tooling (e.g., qdss , Nsight Systems/Compute) Working with large-scale matrix algebra , linear equation solving, iterative solvers, and sparse/dense matrix handling Adapting existing CPU-based simulation code to GPU environments Ensuring numerical stability and precision in GPU-accelerated … handover of GPU-optimized modules Optional: contribution to Jetson-based environments if needed Required Skills Strong experience in CUDA development (custom kernels, memory management, warp optimization) Background in numerical linear algebra , matrix operations, and solving systems of equations Experience with GPU-accelerated libraries such as: cuBLAS, cuSOLVER, cuSPARSE, Thrust , or similar Knowledge of NVIDIA debugging/profiling tools More ❯

holds strong grasp over data science and machine learning concepts, such as multiple of XGBoost/LightGBM/GBM, Isolation Forest, and clustering • Strong background in statistics, probability, and linear algebra as applied to machine learning models. • Experience with SQL language and one or multiple of the following database technologies: Hadoop, Spark, PostgreSQL. • Good knowledge of Linux/ More ❯

to-day, you'll: ? Deliver engaging teaching sessions covering the full ML lifecycle — data prep, model training, evaluation, deployment and monitoring at scale. ? Explain the maths behind ML models (linear algebra, calculus, probability, stats) in an accessible, engaging way. ? Support learners throughout their apprenticeship journey alongside Learner Success Coaches. ? Contribute to content and product development — creating learning materials More ❯

holds strong grasp over data science and machine learning concepts, such as multiple of XGBoost/LightGBM/GBM, Isolation Forest, and clustering • Strong background in statistics, probability, and linear algebra as applied to machine learning models. • Good communication skills • Highly skilled problem solver • Exhibits a high degree of initiative, critical thinking, and system's thinking • Graduate degree More ❯

small-scale system and process improvements to enhance functionality and efficiency. Qualifications Preferred: Applied Machine Learning experience (regression, classification, supervised, and unsupervised learning ) with a strong mathematical foundation in linear algebra , calculus , probability, and statistics. Experience in time-series data analysis , including cleansing and normalization , and experience with scalable Machine Learning ( MapReduce , streaming). Software development expertise in More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯

Technical Areas: Physics: Quantum Mechanics (Must have) Multi-Dimensional Tensor Networks (Must have) Theoretical physics and statistical physics Dynamic systems (energy landscapes, emergent behaviours) Modelling and simulation Mathematics: Advanced linear algebra, optimisation, numerical methods Information theory Probability, statistics Graph theory Highly Valued: Machine Learning Experience: Specifically in parameter-free learning approaches, Bayesian methods and belief networks, reinforcement learning More ❯