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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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learning/analysis. Prior research experience and track record in R&D and standardization. Prior experience in state-of-the-art AI techniques. Mastering of a programming language would be valued (e.g. Python
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
knowledge of the TCP/IP stack and Linux; - proven experience in programming with C++ and/or Python; - research and development experience in the field of computer networks; - solid knowledge on PHY rata
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a related field Experience in python is required Experience in flow chemistry Experience in reactor design is beneficial Experience in process control and PAT Knowledge and interest in closed-loop
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Requirements: Doctoral degree in Chemical Engineering, Chemistry, Computer Science or a related field Proficiency in python and software development Experience with laboratory automation Skilled in database
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microscopy). - Understanding and modification of multi-sensor measurement architecture and control of multiple instruments (Bonsai, LabVIEW). - Data analysis in R and Python and creation of figures
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: Research Topic #1 – Automatic User Requirements Assessment for On-demand Emerging Wireless Networks - solid knowledge of the TCP/IP stack and Linux; - proven experience in programming with C++ and/or Python
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, Python, Java) and good communication, interpersonal, and problem-solving skills are also required for this role. This role meets the eligibility requirements for a skilled worker certificate of sponsorship
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science, GIS, computer science or other relevant disciplines • Good background knowledge and overall interest in design technology • Proficient in statistical and programming skills, like Python
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statistical techniques. Applicants should be fluent in Python, experienced with modern machine learning frameworks such as PyTorch or TensorFlow, and comfortable working with large, complex climate datasets in