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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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working across multiple fields. You will ideally have experience writing historical work or analysing art or architectural representations. Requisites 1st or a 2:1 in Architecture or adjacent
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., StableDiffusion) and large language models (LLMs) based on the transformer architecture [6] (e.g., ChatGPT). In general, the above generative models need considerable amount of computational resources in terms
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Charger). In this role, you will design scalable power converters and intelligent BMS architectures for mobile charging platforms, contributing to the prototyping and real-life testing of a modular mobile
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for tabular-native models. This can involve, for example, studying new TRL model architectures, serialization and tokenization techniques, among others. A strong interest and background in AI and/or NLP
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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analysis systems, to ensure safe and trustworthy results. This can involve research questions from NLP and AI like model robustness and guardrails, human-computer interaction such as interpretability and
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Max Planck Institute for Evolutionary Biology, Plön | Plon, Schleswig Holstein | Germany | 3 months ago
the International Max Planck Research School for Evolutionary PhD, a well-established program with a vibrant research community and outstanding infrastructure. Project 1 - Genetic and adaptive architecture
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efficiency of an algorithm that is run on an architecture/accelerator. To make testing multiple architectures easier, you will leverage our existing approach to generate code from a single source code for
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. They can be constrained by either compute power or memory bandwidth. This information can be used to calculate the theoretical maximum energy efficiency of an algorithm that is run on an architecture