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Employer
- MORE – Laboratório Colaborativo Montanhas de Investigação – Associação
- University of Aveiro
- Centro de Biotecnologia Agrícola e Agro-Alimentar do Alentejo
- Centro de Computação Grafica
- FCiências.ID
- INESC TEC
- Instituto Politécnico de Viana do Castelo
- Instituto Superior de Agronomia
- Instituto de Telecomunicações
- LNEC, I.P.
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Field
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, modeling and simulation of industrial processes. Proficiency in chemical process simulation software (e.g., gPROMS, Aspen, DWSIM, UNISIMDESIGN). Strong programming skills in Python aConditions: The person
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, modeling and simulation of industrial processes. Proficiency in chemical process simulation software (e.g., gPROMS, Aspen, DWSIM, UNISIMDESIGN). Strong programming skills in Python and/or MATLAB
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Engineering, Electronics, Industrial Computing, or related fields, and demonstrate proven skills in: Embedded systems (MCU programming, tinyML); Programming in C/C++ and/or Python; Integration of sensor systems
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using forest inventory data and auxiliary remote sensing data – 10%; v) Experience in programming and tools such as Python, SNAP, C/C++, and R – 5%; vi) Experience in forest modeling and forest growth
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machine learning using chemical compounds— information provided in the CV and/or motivation letter; Knowledge of the Python programming language — information provided in the CV and/or motivation letter
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: Preference will be given to candidates with knowledge of Python programming, web technology development, and data management in IoT environments, particularly those with experience in Kibana, Apache Kafka
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with instrumentation; Experience in applied optics; Proficiency in Python. Funding Entity: project LIBScan, with reference 17490 (COMPETE2030-FEDER-01205900) Co-funded by ERDF - European Regional
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field. priority will be given to candidates with: skills in programming languages and/or tools, such as python, c++, and/or matlab; basic knowledge of artificial intelligence techniques; knowledge
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. Proficiency in programming languages such as Python or MATLAB is essential. Preference is given to candidates with experience in working with large-scale datasets (extended database knowledge and experience
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applied to data science (e.g., R, Python, Bash, and SQL, among others) for processing, statistical analysis, and workflow automation. Practical experience in the processing, analysis, and visualization