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“Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data”. Sequence specific binding and recognition between transcription factors and DNA control gene expression at
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within the development and evaluation of new materials in interaction with biological systems to understand the underlying principles. For us, it is equally important to study the impact of materials
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for an ambitious doctoral student who wants to conduct state-of-the-art research at the crossroads of brain inspired machine learning and next-generation hardware concepts. Candidates should have experience
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of terahertz electronics. In this role, you will work closely with world-class research groups and industrial collaborators, benefiting from state-of-the-art facilities. Your research will push the boundaries
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offers opportunities to participate in teaching and student interaction at both undergraduate and master's level. The project focuses on organic micropollutant removal from wastewater in biological
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year project, funded by the DDLS program, we aim to develop AI-based tools in design of affinity ligands, such as the prediction of binding interactions between proteins. Data-driven life science (DDLS
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Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production - and in the interaction between these areas
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school, which provides foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Through an ambitious program with research visits
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computational facilities for testing & modelling natural clays and access to data on natural slopes in Western Coast of Sweden. These state-of-the-art resources empower you to conduct cutting-edge research with
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Preferred Qualifications: Prior exposure to deep learning frameworks (e.g., TensorFlow or PyTorch) and an understanding of state-of-the-art data preprocessing techniques. Demonstrated experience in