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Field
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project is required to: Develop and implement advanced process and energy system models for integrated, low-carbon, and net-zero energy applications. Conduct research on energy systems integration and
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assembly. If we can model early microbiome development and demonstrate that these models predict real biological behaviour, we move closer to rationally designing interventions that support healthy infant
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, drone-related measurements, and other field sensing platforms. · Conduct data quality assurance and quality control. · Prepare clean, georeferenced, and well-documented datasets for modelling
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contextual scenarios. Develop a unified multimodal detection pipeline that integrates video, audio, and text leveraging fine-tuned Vision-Language Models (VLMs) from WP3, supporting zero-shot reasoning and
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experimentation and data-driven analysis, and who is interested in translating genetic insights into biological mechanisms and therapeutic advances. Research Focus Immune and hematopoietic regulation in type 1
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transition to Net Zero. You will be responsible for developing novel numerical methods to model continuum problems in a two-liquid/granular medium and for interpreting the results physically. These models will
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Stephen & Denise Adams Center for Parkinson's Disease at Yale School of Medicine | New Haven, Connecticut | United States | 2 months ago
-generation AI models of gene regulation and disease progression. Our work is redefining Parkinson’s disease biology and enabling translational breakthroughs. The role Develop deep learning models across genome
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. CALPHAD modelling and thermodynamic assessments. Phase-field modelling of materials behaviour and evolution. The successful candidate will be able to work effectively both independently and collaboratively
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background in power systems, energy modelling, or grid optimisation Proficiency in GUI development (e.g. using Python Dash, Tkinter, or web-based frameworks) Experience working with data-driven simulation and
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are seeking a Postdoctoral Fellow to develop AI-driven methodologies for the digitalization and intelligent management of civil infrastructure. The role focuses on integrating computer vision, machine learning