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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and
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Proficiency in ionic liquid handling and techniques Excellent written and verbal communication skills Ability to work independently and as part of a team Experience with machine learning in engineering is
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++. 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, or similar. Solid expertise in
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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to staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in its research
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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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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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related