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outcomes, while ensuring access to reliable and affordable energy. The EE Lab applies rigorous evaluation and modeling methods, including natural and field experiments, randomized controlled trials
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data · Demonstrated expertise in causal inference and high-dimensional risk adjustment/predictive modeling · Clear scientific writing and communication, an ability to work both independently and in teams
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derived spheroids Develop predictive model of drug response by comparing 2D to 3D cellular systems Testing and validating the relevance of such models in patient tumour specimens Support and preparation
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capacity. We are increasingly incorporating machine learning models into our design-build-test iterations to predict protein function. The group’s focus is predominantly enzyme design, but readily touches
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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-edge in silico, in vitro, and in vivo technologies to understand, predict and treat thrombosis? This is your chance!! Our goal: Develop multi-level thrombosis risk prediction models by integrating
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trends and composition analysis, refractive index determination, and morphology for applications such as environmental monitoring, nuclear non-proliferation, and improving predictive modeling tools (e.g
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industrial partners and spin-offs, together with KTT Business Developers and Innovation Managers. Develop standardized, technology-adapted licensing models and agreement templates (exclusive/non-exclusive
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wide variety of translational neuroscience research programmes. The focus of the role will be analysis of large clinical datasets from PRECISION-ALS (n~20,000) and PRO-ACE to develop prediction models
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction