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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
of Munich (TUM), Campus Heilbronn. We are looking for exceptional candidates who are interested in pursuing a PhD in either theoretical computer science or graph and network visualization. We seek PhD
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into concrete tractable problems you can solve. Be equally passionate about rigorous mathematical reasoning and hands-on Python implementation (PyTorch, JAX, …) in HPC environments. Communicate clearly in English
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languages such as Python or R Ability to conduct independent scientific research Experience working with large datasets is an advantage Excellent communication skills in English (both written and spoken
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experience in remote sensing and programming is a plus (e.g., using QGIS or R) Good communication skills Ability to work in a team Your tasks You will conduct research on the use of remote sensing data
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networking, enable internationalization and mobility, and create a collaborative environment. TUM and the CRC embody a university culture that is characterized by cosmopolitanism, mutual appreciation, thriving
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, natural hazards management or related fields Interested in protective forests and their management Good quantitative skills (e.g., data analysis, simulation modelling, remote sensing) Good communication
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involved in teaching at under/postgraduate level as well as funding acquisition and (global) outreach, if desired. International networking and collaborations are regarded as an integral part of the PhD
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on an important topic in a well-funded multi-disciplinary international training network. The training involves multiple activities, in addition to your research, and secondments across our partners. Overview
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03.06.2025, Wissenschaftliches Personal Chemical signaling, the most ancient and widespread form of communication, plays a crucial role in maintaining species boundaries through exclusive
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning