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-motivation and interest to learn new skills Great to have: Experience programming in Python, Julia, or C/C++ Experience with Mathematica Experience with finite element methods, agent-based simulations, and/or
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to reason about software (e.g., LLM agents for finding and fixing bugs)Static and dynamic program analysis (e.g., to infer specifications)Test input generation (e.g., to compare the behavior of old and new
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potential projects: Development of modern auto-differentiation (JAX-based) physics simulators for the discovery of new physics experiments) Developing, benchmarking and advancing state-of-the-art AI-driven
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), mathematical evolutionary modeling (game theory, dynamical systems, agent-based simulations or other), bespoke probabilistic modeling / (Bayesian) data analysis (e.g., in the Rational Speech Act framework
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Project The PhD project DC7 aims to develop and apply a coupled Agent-Based Model (ABM) and couple it to the Regional Flood Model (RFM) to evaluate the effect of adaptive behaviour of small and medium-sized
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– Multiscale imaging of organ-specific inflammation” and aims to develop and apply a quantitative MRI approach, by establishing a data base of simulated and experimental MR data of tissue and single cells
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) develop participatory planning methods based on the technical outcomes from the digital twin to create future scenarios for responsible mobility that are technically well-grounded and at the same time
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relevance. Your Tasks Antibody–Drug Conjugates (ADCs) are a rapidly advancing class of cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic agents. Upon
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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the MATSim agent-based transport simulation framework. The main task is to enable simulated agents to choose transportation modes, such as car, bus, bike, or walking, based on real-time feedback from