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pediatric gliomas, with a focus on patient-derived tumor models (including organoids and PDX) and functional genomics approaches (e.g., CRISPR-based perturbation). The role will include routine mammalian cell
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/R, machine learning frameworks, and dashboarding tools (e.g., Streamlit, Superset, Grafana, PowerBI). Familiarity with various types of databases, including NoSQL (e.g. MongoDB), graph databases (e.g
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
abiotic resources. We integrate remotely sensed information with in-situ data, process-based models, and leverage satellite communication, IoT and machine learning technologies in order to provide evidence
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approaches such as physics-informed machine learning (PINNs), graph neural networks for lattice structures, and neural operators for fast surrogate modeling, as well as AI-driven inverse design and generative
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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are looking for a candidate with a background in machine learning or data science and strong software engineering skills. The Ph.D. position is a part of the strategic research area in IT and mobile
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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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). Experience with machine learning algorithms or predictive modeling. Experience preparing analytic code, documentation, and supporting materials for publication. Ability to produce clear analytical
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, Python, Julia, and/or MATLAB Familiarity with machine learning methods and algorithmic modeling for large datasets Experience with LaTeX, Unix/Linux Evidence of outstanding academic achievement Familiarity
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Artificial Intelligence, Machine Learning, Computer Science, Telecommunications, or equivalent degree. Minimum of 3 years of postdoctoral research experience. Proven experience in various AI methodologies