Aktuelle Jobs im Zusammenhang mit AI Architect and Optimization Specialist - Münster, Nordrhein-Westfalen - pacemaker
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AI Architect and Optimization Specialist
vor 2 Monaten
We are seeking a highly skilled AI Architect and Optimization Specialist to join our team at Pacemaker. As a key member of our data science team, you will be responsible for designing, implementing, and optimizing neural network architectures and deep learning models for various applications.
Key Responsibilities- Design and Implement Deep Learning Models: Develop and apply advanced mathematical models, including optimization techniques like Particle Swarm Optimization (PSO), Genetic Algorithms, and other heuristic methods, for engineering and computational problems.
- Lead Research and Development Efforts: Collaborate with cross-functional teams to address high-impact business challenges, leveraging your expertise in deep learning, machine learning, and mathematical optimization.
- Analyze Large-Scale Data Sets: Extract meaningful insights, trends, and patterns using machine learning techniques and provide actionable recommendations to stakeholders.
- Collaborate with Domain Experts: Work with engineering teams to integrate machine learning models into production systems and ensure seamless deployment.
- Stay Up-to-Date with Industry Developments: Continuously update your knowledge of the latest advancements in AI, deep learning, and optimization algorithms and apply this knowledge to improve model performance.
- Mentor Junior Team Members: Share your expertise and experience with junior data scientists and machine learning engineers to help them grow in their careers.
- Master's Degree in Mathematics, Computer Science, or a Related Field: A PhD is a plus.
- Minimum 5 Years of Professional Experience: In data science, machine learning, or a related field with a focus on neural networks, deep learning, and optimization models.
- Proficiency in Deep Learning Frameworks: Experience with frameworks such as TensorFlow, PyTorch, Keras, etc.
- Strong Mathematical Background: Linear algebra, calculus, probability, and optimization are essential.
- Hands-On Experience with Optimization Algorithms: Familiarity with algorithms like PSO, Genetic Algorithms, Simulated Annealing, etc.
- Strong Understanding of Machine Learning Algorithms: Supervised, unsupervised, reinforcement learning, and practical experience in applying them to solve real-world problems.
- Familiarity with Data Processing Libraries: NumPy, Pandas, scikit-learn, XGBoost, etc.
- Excellent Communication and Collaboration Skills: Ability to work effectively in cross-functional teams and communicate complex ideas to stakeholders.
Pacemaker is a dynamic startup that works with passion and ambition on AI-based solutions to make data-driven decisions for a sustainable supply chain.
