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infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), particle sizing, and zeta potential. Knowledge of aquaculture, veterinary drugs, and Portuguese communication skills will be valued. How
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. Mandatory requirements: Demonstrated knowledge of machine learning techniques and programming, with extensive experience in data analysis using Python and R. PhD in Economics, with expertise in health
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for the fellowship requirements published by FAPESP (https://fapesp.br/en/postdoc ). Desirable requirements: Familiarity with audiovisual devices; proficiency with audiovisual technology and video editing; interest in
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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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nutritional analyses, together with knowledge of experimental design and statistical analysis, is required. Desirable requirements: Candidates should have a strong publication record in indexed international
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Diseases (PNAIPDR). Therefore, it is desirable for the candidate to have knowledge of the PNAIPDR. How to apply: The scholarship is open to both Brazilians and foreign nationals. Each candidate must send
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qualifications include: 1. Experience in electrochemical sensors or biosensors; 2. Knowledge of voltammetric and/or electrochemical analytical techniques; 3. Experience with nanostructured materials, printed
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with ab initio calculations or mineral equations of state; familiarity with MAGEMin, BurnMan; knowledge of Bayesian methods (MCMC) or neural networks applied to geophysical problems; publications in
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Sciences, Geography, Law, or related fields; demonstrated experience in Nature-based Solutions and climate-related research, qualitative research, and fieldwork; knowledge of semi-structured interviews
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to organize data, document analyses, prepare technical reports, and contribute to the writing of scientific manuscripts in English; ii) Knowledge of programming or scripting languages such as R, Python, Perl