1 to 25 of 76 Permanent Linear Algebra Jobs

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 ❯

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 ❯