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Field
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and optimising assays aimed at target validation; principally through immunogenicity assays in animal models. You will also conduct experiments aimed at understanding the tumour-immune microenvironment
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex
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) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
simulation tool for modelling hydrogen isotope transport in materials. The role is central to enhancing the scientific reliability and benchmarking capacity of FESTIM in support of fusion fuel cycle modelling
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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of the quark-gluon plasma, low-energy phenomenology within and beyond the Standard Model, neutrino physics and astrophysics, nuclear astrophysics, nuclear structure, perturbative QCD, and quantum few- and many
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: We would like to recruit a postdoctoral fellow in cancer outcomes research, with a particular interest in decision/simulation modeling and cost-effectiveness analysis. The ideal candidate will have
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modelling. It is expected that the degree of involvement in meetings and organisational tasks related to research projects will increase steadily during the employment. The following qualifications are highly
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Diseases, School of Medical Sciences, Faculty of Medicine and Health. You will design in vitro and microfluidic-based model systems to evaluate vascular interactions related to medical device complications
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time