32 python "IDAEA CSIC" Fellowship positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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responsibility is to build and validate these offloading strategies, complete with Python application programming interfaces (APIs), through rigorous simulations and proof-of-concept (PoC) trials. This position is
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will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python
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materials characterization (e.g., SEM, XRD, FTIR, mechanical testing). Proficiency in data analysis tools (Python, MATLAB, JMP) and technical documentation. Strong analytical skills and attention to detail
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents
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within the team and the customers. Be self-directed to fulfil the project requirements. Job Requirements: Competence in working with languages such as C along with AI applications as well as Python-based
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Python. Hands-on experience with: PyTorch Hugging Face Transformers OpenCV Experience working with deep learning models for image or video analysis.
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Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry-collaborative research Where to apply Website https://www.timeshighereducation.com/unijobs/listing/413018/research-fellow
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background in power systems, energy modelling, or grid optimisation Proficiency in GUI development (e.g. using Python Dash, Tkinter, or web-based frameworks) Experience working with data-driven simulation and
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Learning Video Understanding Multimodal AI Excellent programming skills in Python. Hands-on experience with: PyTorch Hugging Face Transformers OpenCV Experience working with deep learning models for image