8 of 8 SciPy Jobs in England

processing: Kubernetes, Spark â Experience with Java 2D graphics and 3D OpenGL programming. â Experience with scientific computing libraries and frameworks: o Python: NumPy, SciPy, Pandas, TensorFlow (for ML/AI) o C Java: CUDA (for GPU acceleration) o Angular or React o Microservice: Quarkus, Spring Boot, AWS API Gateway

unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks. ? Experience with ML Ops, including model deployment, monitoring

labeling experts and technical teams Required Qualifications - Statistical Expertise : Strong foundation in statistical analysis, hypothesis testing, and pattern recognition - Programming : Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis - Data Analysis : Experience with exploratory data analysis and creating actionable insights from complex datasets - AI/

e.g UVM/OSVVM Embedded software development in C, C++ or similar Digital Signal Processing System modelling e.g. Matlab/Python/Numpy/Scipy Agile/Scrum Configuration control, Continuous Integration and Continuous Development etc Domain expertise in a relevant technology area Technical/Team leadership If this

electronics, physics or computer science and have significant skills and experience in the following areas: - Expert Python proficiency for data analysis and prototyping (NumPy, SciPy, PyTorch). - Demonstrable applied experience of digital signal processing (FFT, modulation, noise modelling, resampling). - Experience with SDR frameworks (GNU Radio, SDRangel, SoapySDR). - Understanding

Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company … some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI, science

accuracy Developing ML and statistical models for RF classification, anomaly detection, and emitter identification Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration via ZMQ, GNU Radio, or C++ backends Leading RF data collection, field experiments, and over-the-air testing using drones, wireless … devices, and custom transmitters Requirements Strong Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch) Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling) Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR Practical understanding of RF hardware chains

accuracy Developing ML and statistical models for RF classification, anomaly detection, and emitter identification Prototyping real-time or batch-processing systems in Python (NumPy, SciPy, PyTorch) with potential integration via ZMQ, GNU Radio, or C++ backends Leading RF data collection, field experiments, and over-the-air testing using drones, wireless … devices, and custom transmitters Requirements Strong Python proficiency for RF data analysis and prototyping (NumPy, SciPy, matplotlib, scikit-learn, PyTorch) Solid understanding of DSP fundamentals (FFT, filtering, modulation, correlation, noise modelling, resampling) Familiarity with SDR frameworks such as GNU Radio, SDRangel, osmoSDR, or SoapySDR Practical understanding of RF hardware chains