1 to 25 of 49 Keras Jobs in the South West

of hands-on experience in AI and machine learning. Technical Expertise: Strong proficiency in Python. Expertise in machine learning frameworks such as TensorFlow, PyTorch, Keras, or similar. Machine Learning: Solid understanding of supervised and unsupervised learning, deep learning (CNNs, RNNs, GANs), and reinforcement learning. Data Handling: Proficient in data manipulation More ❯

preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. More ❯

preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. More ❯

preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. More ❯

track record in the successful development and deployment of AI/ML-based tools. Strong command over Python and major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn. Extensive experience in building and implementing predictive models to design biological sequences and/or analyse biological sequence data (DNA More ❯

track record in the successful development and deployment of AI/ML-based tools. Strong command over Python and major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn. Extensive experience in building and implementing predictive models to design biological sequences and/or analyse biological sequence data (DNA More ❯

track record in the successful development and deployment of AI/ML-based tools. Strong command over Python and major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn. Extensive experience in building and implementing predictive models to design biological sequences and/or analyse biological sequence data (DNA More ❯

track record in the successful development and deployment of AI/ML-based tools. Strong command over Python and major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn. Extensive experience in building and implementing predictive models to design biological sequences and/or analyse biological sequence data (DNA More ❯

track record in the successful development and deployment of AI/ML-based tools. Strong command over Python and major ML frameworks such as Keras, PyTorch, TensorFlow, or Scikit-Learn. Extensive experience in building and implementing predictive models to design biological sequences and/or analyse biological sequence data (DNA More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

. we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one More ❯

techniques Able to apply machine learning to real-life problems Vision Transformers, DeepLabv3, SegFormer, etc.Python, Scikit-Learn, NumPy, Pandas and PyTorch/TensorFlow/Keras Thrive in a cross-functional working environment Possess a clear passion for data science beyond the day job Nice-to-haves: On offer : Base salary More ❯

methods using both quantitative and qualitative metrics. Strong mathematical and statistical background. Deep knowledge of Python and data science packages such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder More ❯

methods using both quantitative and qualitative metrics. Strong mathematical and statistical background. Deep knowledge of Python and data science packages such as Scikit learn, Keras, Tensor flow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, business analysts. Familiar with MLOps industry best practices. Good stakeholder More ❯

methods using both quantitative and qualitative metrics. Strong mathematical and statistical background. Deep knowledge of Python and data science packages such as Scikit-learn, Keras, TensorFlow, and PySpark. Experience and understanding of mixed technical teams such as engineering, architects, and business analysts. Familiar with MLOps industry best practices. Understanding of More ❯

equivalent industry experience. Deep expertise in voice conversion, generative models, deep learning, or statistical modeling. Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in Python and C/C++. Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow). Proven ability to manage GPU More ❯

equivalent industry experience. Deep expertise in voice conversion, generative models, deep learning, or statistical modeling. Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in Python and C/C++. Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow). Proven ability to manage GPU More ❯

equivalent industry experience. Deep expertise in voice conversion, generative models, deep learning, or statistical modeling. Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in Python and C/C++. Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow). Proven ability to manage GPU More ❯

equivalent industry experience. Deep expertise in voice conversion, generative models, deep learning, or statistical modeling. Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in Python and C/C++. Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow). Proven ability to manage GPU More ❯

equivalent industry experience. Deep expertise in voice conversion, generative models, deep learning, or statistical modeling. Strong hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras). Proficiency in Python and C/C++. Experience with scalable data tools (e.g., PySpark, Kubernetes, Databricks, Apache Arrow). Proven ability to manage GPU More ❯