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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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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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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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Experience with multimodal machine learning, variational autoencoders, and/or similar machine learning or AI models implemented in Python. Responsible for implementing components of research design analysis
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modeling and skilled in numerical simulations, will design a mesoscopic radiative model aimed at overcoming the CFL constraint and exploring machine learning methods and neural operators to address
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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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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 14 hours ago
provide evidence of experience applying data science, statistical modelling, machine learning, or bioinformatics methods within research or applied settings, ideally alongside the development of reliable
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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
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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