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essential. Good programming skills in at least one programming language (e.g., Python). Experience with machine learning, LLMs, or HCI/user study methodologies will be a plus. Strong interest in acquiring and
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models. The scientist will conduct research using machine learning and classical parameterization methods on data from ocean gliders equipped with microstructure turbulence sensors, turbulence resolving
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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Biology, Computer Science or related studies) Experience in Python with PyTorch (or equivalent) programming Experience in sequencing data analysis Basic knowledge in machine learning Experience with linux
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looking for student assistants: Leibniz-Project LAB2 (lead by Dr. Levent Neyse) and DFG-Project ‘Mental Models and Discrimination’ (lead by Kai Barron, PhD). Please note: The list of tasks and duties below
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. Specifically, the PhD candidate is expected to contribute corpora preparation (collection and organizing the annotation), use machine learning approaches for irony detection, and testing for experimental and
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machine learning methods based on specific problems The targeted visualization of results is also within your area of responsibility What you bring to the table You are studying physics, mathematics
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission