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Field
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techniques and AI, engineer robust and adaptive solution systems, and address challenges related to multi-agent coordination and decision-making under uncertainty. The ideal candidate will have a strong
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high infrastructure costs. We will explore (1) learning-based techniques (e.g., LLMs, agents) to capture the intent behind code changes, (2) defining new metrics for test "quality" that go beyond code
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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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, an agent-based physiological 3D model of mitochondria based on the seminal work of Prof. Skupin (https://www.nature.com/articles/s41598-019-54159-1 ) will be extended and embedded into a continuous
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for anomaly detection, risk analysis and autonomous navigation. The resulting AI-based agents and systems can support maritime situational awareness, security, safety and logistics. The PhD candidate will
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agents. Your research will explore how reinforcement learning, multi-agent cooperation and generative worldmodels can deliver adaptive strategies that thrive amid volatile, multi-asset markets, micro
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created from extracellular matrix (ECM)-based hydrogels or from porous titanium will be fabricated using additive manufacturing and designed to incorporate antimicrobial agents to reduce the risk of
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks
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of contaminated soils on soil strength and erosion control. Developing a Model for the Adoption of Agentic AI in Facilities Management: Enabling Autonomous Decision-Making for Sustainable Built Environments
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and computer science, connected to MDU’s existing research areas. The PhD project focuses on the development of a large language model (LLM)-based AI agent specialized in energy decision-making