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Field
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. Your expertise includes machine learning techniques such as Bayesian optimisation, and you’re comfortable working with experimental data, high-performance computing environments, and (ideally) thin film
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and motivation and submit an innovative research proposal, which will be funded individually. Target Group students in Master's or PhD programmes from the following subjects: natural science pharmacy
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melanoma and synovial sarcoma. Despite this tremendous progress, there is still an urgent need to identify and exploit new, tumor-specific targets. Many tumors escape treatments, such as checkpoint
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 21 hours ago
of the midbrain dopaminergic system during decision-making, and in several of its direct and indirect targets (prefrontal cortex, cingulate and striate cortex). The project's work plan is based on optical
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Basque Center for Macromolecular Design and Engineering POLYMAT Fundazioa | Spain | about 21 hours ago
the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
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basic science with biotech applications and combines rich functional assays with cutting-edge computational biology. Our method relies on aptamer-based sensors to recognise each specific metabolite target
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to meet your organization’s goals. Whether through targeted research areas, internship placements, or specialized mentorship opportunities, our program adapts to support your workforce and innovation
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the research project - “Development of a novel dendritic cell-targeted vaccination approach for treating mesothelioma”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center