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Python programming and relevant libraries (e.g., OpenCV, NumPy, scikit-learn, Pandas, Matplotlib) Experience with dataset preparation, model training, and performance evaluation Candidates with strong
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tools such as MATLAB, Python, or equivalent engineering software. Experience with experimental and instrumentation measurements setup for noise and vibration (e.g., sound intensity probes, vibration
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reality-gap mitigation. Integrate simulations with ROS / ROS 2 for closed-loop robot control, testing, and benchmarking. Automate simulation setup, execution, and experiment management using Python and C
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highly encouraged. Proven track record in research and development of machine learning algorithms. Proficiency in algorithm development using Python and ML frameworks such as PyTorch or TensorFlow. Key
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techniques. Training or demonstrated experience in remote sensing, spatial data collection, and thematic mapping. Proficiency in data analysis software (e.g., R, MATLAB, SPSS, Primer, Python). Experience with
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. Knowledge of visual generation and multimodal LLM will be advantageous. Key Competencies Proficiency in programming languages (e.g., Python and C++), and familiarity with AI frameworks (e.g., PyTorch). Self
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timely completion of all project deliverables. Implement and enhance GeoTOPSIS/VectorMCDA algorithms within QGIS using Python or equivalent programming frameworks. Develop a user-friendly interface (QGIS
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control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with the microgrid model and validate performance under different simulated conditions. Conduct
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-based applications (e.g., AWS, Google Cloud, Microsoft Azure), including containerization (Docker), orchestration (Kubernetes), serverless computing, and REST API development. Proficient in Python, with
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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch