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Job Description Are you driven by the potential of AI in enzyme engineering? Do you want to design novel enzymes from scratch using cutting-edge generative models and validate their function in
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sequencing data and optimise editing conditions Execute pooled functional screens to identify synergistic gene combinations Validate hits with targeted assays and in‑vitro models Contribute to B.Sc./M.Sc
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and what they mean for the system’s efficiency and safety. You will develop models of AI bidding strategies, analyze strategic interactions using game theory, and design optimization methods to identify
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
the furthermore, core works are envisioned in (c) initiating novel VLE design using foundational Digital Twin for Construction Safey (DTCS) components (e.g., city models, nD BIM, IoT/sensor data, TPTC, LPS, LBS
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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a team of 25 colleagues dealing with CCUS, and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Apply AI in context of capture solvent modelling Understand and analyse
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market frameworks and business models for fair value distribution will be analysed. Responsibilities and qualifications Your primary research tasks will include: Develop and simulate coordinated control
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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collaboration that covers all aspects of our research: theory and modeling, sample growth and fabrication, experiments and demonstrations. We have created a dynamic research environment of young and senior