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models for in silico screening. Lastly, they will coordinate activities with partners in Scotland and Denmark. Mandatory requirements: Ph.D. completed within the last 7 years in fields such as Immunology
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in molecular biology, with emphasis on primer design and validation and gene expression analyses. Proficiency in statistical analyses, including multivariate approaches and mixed models. Established
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; - Proven experience in an academic or industrial laboratory setting in medicinal chemistry synthesis and chemical fragment screening; - Experience in data analysis and integration, and molecular modeling
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in English explaining your fit with Axis 1; and three publications authored by the applicant, preferably indexed journal articles. Final thesis chapters in manuscript form are also accepted. Subject
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to staff position within a Research Infrastructure? No Offer Description Activities: The post-doctoral researcher will develop machine learning models to evaluate the effectiveness and cost
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-mantle NCMASF3 system; ii) Training a Mixture Density Network emulator on 106 thermodynamic evaluations; iii) Implementing a global MCMC Bayesian inversion of the SPARTANS tomographic model; and iv
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in a dynamic and internationally recognized group. Activities include generation of genetically-modified mouse models, isolation and in vitro culture of mouse germ and embryonic cells, molecular
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years. Demonstrated research experience in cropping systems, evidenced by peer-reviewed scientific publications. Experience in data analysis and modeling using R and/or Python. Knowledge of statistical
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summary (FAPESP model ) and an updated Lattes CV; b) A motivation letter demonstrating how the candidate meets the required qualifications and possesses the required skills (maximum of 500 words); and (c) A
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@cpqba.unicamp.br ), researcher at CPQBA, with the following documents: - Cover letter (3-page max), including a description of your research background and interests; - Curricular summary (according to FAPESP model