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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
Area of research: Scientific / postdoctoral posts Starting date: 01.07.2025 Job description: Postdoc (f/m/d): Machine Learning for Materials Modeling With cutting-edge research in the fields
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teaching and curriculum development. Your qualifications PhD in computer science, data science, applied mathematics, physics, or a related field. Strong expertise in machine learning and deep learning
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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Your Job: You will develop impactful machine learning techniques to deal with complex quantum states. Possible research directions and tasks include: Method development to advance neural quantum
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[map ] Subject Areas: Machine Learning/Deep Learning; Optimization, Combinatorics, Polyhedral geometry, Algebraic geometry Appl Deadline: 2025/05/01 11:59PM (posted 2025/03/19, listed until 2025/09/19
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The KU Faculty of Mathematics and Geography’s research group Mathematics - Scientific Computing at the Mathematical Institute for Machine Learning and Data Science (MIDS) is seeking highly motivated
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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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-08187-1 Your Profile: Master and PhD in biology, genomics or bioinformatics Strong background in data science or machine learning (deep learning, statistical modeling, or large-scale data analysis a plus
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Your Job: In this position, you will be an active part of our Simulation and Data Lab for Applied Machine Learning. Within national and European projects, you will drive the development of cutting
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its detailed analysis through Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap