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to study complex biophysical processes on long timescales. We use data-driven methods for systematic coarse-graining of macromolecular systems, to bridge molecular and cellular scales. We work on a
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organismal systems across diverse biological contexts. Using a blend of cutting-edge experimental and computational approaches, we strive to decode and model complex biological processes—from single-cell
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, interactive scenarios. The goal is to understand how primates attribute perception to others do, based on their gazes and postures. Our approach dealing with complex brain and behavior data from these scenarios
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quantitative and mechanistic insights into how complex biological processes operate and coordinate in the context of cells, organisms and communities and across temporal, spatial and evolutionary scales. Within
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tackle the complex challenges of our time. What you will do Development and optimization of integrated RF circuitry for Radar Transceivers for implementation into 2.5/3D heterointegrated systems Design of
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intelligence, automation of processing chains and the creation and management of databases • Ability to analyze complex problems, develop innovative solutions and understand the requirements of research projects
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transfer. Its current five-year scientific programme, Molecules to Ecosystems (2022–2026), aims to deepen our understanding of life — from molecular mechanisms to complex ecosystems — with a strong emphasis
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 4 hours ago
complex scientific problems – as well as the ability to stay focused on long-term research goals – is essential for success in this role. While the position is fully funded, applicants will be strongly
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to the the Genome Biology Unit at EMBL, a research department composed of ~10 independent research groups, using and developing genomics methods to decode and model complex biological processes—from single-cell
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of Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs), with a strong emphasis on deploying these models on hardware-constrained edge platforms. The position focuses on developing robust