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, data science using EHRs, machine learning, data mining, and natural language processing are preferred. Job Description: Develop large language models and other methods and tools to effectively use
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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-impact problems. The ideal candidate will have a strong grasp of diverse machine learning techniques and a passion for experimenting with model architectures, feature engineering, and hyperparameter tuning
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid
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machine learning for next-generation wireless networks, (ii) Foundations of semantic communications and age of information, (iii) Stochastic geometry and spatial modeling of large-scale wireless systems
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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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the development of components using agent-based modeling techniques, reinforcement learning, and other explainable artificial intelligence modules for decision-making, situation assessment, and operational support
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Robotisation (PROMAR) group, headed by Matthias Rupp. The group develops fundamental and technological expertise in machine learning for materials science, including data-driven accelerated simulations and
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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/Administrative Internal Number: 527353 Pay Grade/Pay Range: Minimum: $62,300 - Midpoint: $81,000 (Salaried E10) Department/Organization: 214251 - Electrical and Computer Eng Normal Work Schedule: Monday - Friday