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the reference number 27697, via our online portal: Apply now via https://jobs.uksh.de/job/Kiel-PhD-%28mfd%29-Statistical-Genetics-Machine-Learning-Schl-24105/1279933701/ For more information visit: www.uksh.de
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, electrical engineering or simila,r with an affection for machine learning; You are an independent and original thinker with a creative mindset; You are a fast thinker with excellent analytical and
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composites for enhanced durability, performing microstructural analysis and mechanical testing. Topology Optimization & AI Integration: Use AI and machine learning to guide structural and topology optimization
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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of mathematics, machine learning, proficiency in programming languages including Python, C/C++. Experience with machine learning. Excellent academic background. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS
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Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through
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engineering Engineering » Simulation engineering Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 5 Feb 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Not
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
establish reference performance and guide the design of more advanced models. The core of the work will then focus on developing and evaluating machine-learning-based NILM methods tailored
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Analysis and Decision Support - Applying statistical and machine learning methods to interpret the data, identify trends for optimizing aquaculture conditions.; • Experimental Validation - Conducting
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem