26 to 50 of 70 SciPy Jobs in the UK excluding London

exposure, generation patterns, demand profiles, and commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer ...

exposure, generation patterns, demand profiles, and commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer ...

exposure, generation patterns, demand profiles, and commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer ...

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 ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...

dataset splits, overfitting, evaluation metrics, augmentation, and performance trade-offs. Comfortable working with image processing and common computer vision libraries (e.g., OpenCV, NumPy, Pillow, SciPy, Matplotlib). Good engineering habits: Git-based workflows, virtual environment usage, clean code, and reproducible experiments. Strong problem-solving ability, able to iterate quickly ...