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Field
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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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of computer vision algorithms, with a focus on assessing performance in edge deployment settings. Their role will involve supporting model development, running experiments, and analyzing results to evaluate
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with such models. Experience with machine learning methods applied to biological data. Familiarity with large language model APIs and frameworks (e.g., Claude/Anthropic API, OpenAI API, LangChain
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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conferences in robotics, control, or machine learning. • Excellent communication skills and ability to independently publish and present research outcomes. • Demonstrated ability to supervise students
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual cell, foundation models, graph neural networks, or multimodal omics integration). Epigenetics (DNA methylation
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economic modeling, with interests including improved spatial resolution and machine-learning-enabled approaches for policy analysis. Postdoctoral Position (f/m/d) – Integrated Assessment Modeling (Climate
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to improve the early detection of pancreatic tumors and improve patient management. Different machine-learning approaches will be compared, and models validated on data prospectively collected. The position
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We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025 BRIDGES project GenePPS, which investigates how machine learning can enable prediction of gene perturbation