1 to 25 of 54 Linear Algebra Jobs in London

some blog posts about your discipline, or perhaps even delivered a talk or two that you’d like to share. Skills, Knowledge and Expertise Technical skills Good understanding of linear algebra (vectors, matrices, tensors etc.) and statistics (probability distributions, probability, bayesian stats etc.) Good working knowledge of Python Implementing end-to-end ML pipelines Hand-on experience in More ❯

Proven track record of developing and implementing successful AI-driven solutions, ideally within the financial services industry. Strong understanding of the mathematical foundations of deep learning, including multivariate calculus, linear algebra, and optimization techniques. Proficient in Python and deep learning frameworks such as TensorFlow and PyTorch. Experience with CUDA kernels and GPU profiling is a plus. Excellent communication More ❯

Proven track record of developing and implementing successful AI-driven solutions, ideally within the financial services industry. Strong understanding of the mathematical foundations of deep learning, including multivariate calculus, linear algebra, and optimization techniques. Proficient in Python and deep learning frameworks such as TensorFlow and PyTorch. Experience with CUDA kernels and GPU profiling is a plus. Excellent communication More ❯

Proven track record of developing and implementing successful AI-driven solutions, ideally within the financial services industry. Strong understanding of the mathematical foundations of deep learning, including multivariate calculus, linear algebra, and optimization techniques. Proficient in Python and deep learning frameworks such as TensorFlow and PyTorch. Experience with CUDA kernels and GPU profiling is a plus. Excellent communication More ❯

experience), or equivalent experience Track record of delivery of outstanding research using deep learning techniques, including designing new ML architectures, hands-on experimentation, analysis, and visualisation Strong knowledge of linear algebra, calculus, probability, and statistics Demonstrated ability to write clean, idiomatic, and highly performant Python code Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific More ❯

quantitative researcher, quantitative analyst or another relevant role Degree in Applied Mathematics, Computer Science, Financial Engineering, Technology or Engineering Knowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methods Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models More ❯

a data scientist, quantitative researcher, or similar role. Degree in Applied Mathematics, Computer Science, Financial Engineering, Technology, or Engineering. Knowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methods. Experience with models like regression, tree-based models, support vector machines, PCA, clustering, matrix factorization, deep learning, etc. Proficiency in Python and its machine More ❯

experience), or equivalent experience Track record of delivery of outstanding research using deep learning techniques, including designing new ML architectures, hands-on experimentation, analysis, and visualisation Strong knowledge of linear algebra, calculus, probability, and statistics Demonstrated ability to write clean, idiomatic, and highly performant Python code Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific More ❯

in AI engineering, machine learning, or a similar role, preferably within the finance industry or at a leading technology company. Strong expertise in algorithms, data structures, multivariate calculus, and linear algebra. Proficient in Python, TensorFlow, PyTorch, or similar languages and frameworks, with experience writing CUDA kernels and profiling GPU code a plus. Excellent communication skills, with the ability to More ❯

in AI engineering, machine learning, or a similar role, preferably within the finance industry or at a leading technology company. Strong expertise in algorithms, data structures, multivariate calculus, and linear algebra. Proficient in Python, TensorFlow, PyTorch, or similar languages and frameworks, with experience writing CUDA kernels and profiling GPU code a plus. Excellent communication skills, with the ability to More ❯

in AI engineering, machine learning, or a similar role, preferably within the finance industry or at a leading technology company. Strong expertise in algorithms, data structures, multivariate calculus, and linear algebra. Proficient in Python, TensorFlow, PyTorch, or similar languages and frameworks, with experience writing CUDA kernels and profiling GPU code a plus. Excellent communication skills, with the ability to More ❯

data scientist, quantitative researcher, quantitative analyst or another relevant roleDegree in Applied Mathematics, Computer Science, Financial Engineering, Technology or EngineeringKnowledge of probability theory, inferential statistics, machine learning, Bayesian statistics, linear algebra, and numerical methodsExperience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g. More ❯

LIDAR. Experience with sensor characterisation and modelling. Familiarity with classical and contemporary approaches for state estimation and tracking (e.g. Kalman Filter, UKF, Particle Filter). Sensor calibration. Background in linear algebra and probability/stochastic processes. Familiarity with non-linear optimisation frameworks like Ceres, g2o, GTSAM, etc. Probabilistic inference and Bayesian likelihood estimation (e.g., Markov Chain Monte More ❯

build and make predictions with those models Strong understanding of machine learning approaches and algorithms (e.g. LSTM, Markov-Chain Models etc) Advanced understanding of Statistical/Probabilistic programming and Linear Algebra Be proficient in (Python, SQL) programming for Data Science and Time Series analysis Have in depth understanding of statistical modelling/ML techniques for time series forecasting More ❯

Engineering, Statistics, or equivalent fields. Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning). Experience with prototyping languages such as Python and R. Strong mathematical background (linear algebra, calculus, probability and statistics). Experience with scalable ML (MapReduce, streaming). Ability to drive a project and work both independently and in a team. Smart, motivated More ❯

and translating research into practical solutions for predictive analytics. Experience in solution design, architecting and outlining data analytics pipelines and flows. Advanced Mathematics skills including experience with Bayesian statistics, linear algebra and MVT calculus, advanced data modelling and algorithm design experience. Design and deployment experience using Tensor Flow, Spark ML, CNTK, Torch or Caffe. The perks A flexible More ❯

of Python and modern ML libraries like PyTorch, Tensorflow, or JAX. Ability to implement models from academic papers like ICML, ICLR, NeurIPS, etc. Strong quantitative intuition and mastery of linear algebra, probability and statistics. Solid knowledge of basic data structures (list, vector, stack, heap), basic algorithms (sort, search, etc.) and associated time/memory asymptotic complexity. Maybe you More ❯

publications, or contributions to machine learning codebases. Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas Strong knowledge of linear algebra, calculus and statistics Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics) Experience working with biological or chemical data and biological or chemistry software More ❯

experience in a technical field. A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation. Strong knowledge of linear algebra, calculus and statistics. Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas. A passion for applying ML More ❯

Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting from ML experiments. Mathematics skills to support the above: calculus, probability theory and linear algebra. Desirable Experience in one or more of: distributed computing, efficient computing based on low-precision arithmetic, deep learning models including large generative models for language, vision and other More ❯

analysis, machine learning, and optimization. Strong programming skills in Python, including libraries like NumPy, Pandas, and Scikit-learn. Familiarity with Q/KDB and Git. Strong mathematical ability in linear algebra and calculus. More ❯

video security use cases, and convert those ideas to working code. Requirements You should be a good software engineer who enjoys writing production-grade software. Strong machine learning fundamentals (linear algebra, probability and statistics, supervised and self-supervised learning). Keeping up with the latest in deep learning research, reading research papers, and familiarity with the latest developments More ❯

video security use cases, and convert those ideas to working code. Requirements You should be a good software engineer who enjoys writing production-grade software. Strong machine learning fundamentals (linear algebra, probability and statistics, supervised and self-supervised learning) Keeping up with the latest in deep learning research, reading research papers, and familiarity with the latest developments in More ❯

video security use cases, and convert those ideas to working code. Requirements You should be a good software engineer who enjoys writing production-grade software. Strong machine learning fundamentals (linear algebra, probability and statistics, supervised and self-supervised learning) Keeping up with the latest in deep learning research, reading research papers, and familiarity with the latest developments in More ❯

experience in a technical field. A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation. Strong knowledge of linear algebra, calculus and statistics. Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas. A passion for applying ML More ❯