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Field
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-level optimisation using TRNSYS and MATLAB/Python. The associate will use advanced characterisation (e.g., DVS, porosimeter, DSC/LFA) to develop and assess adsorption composites, and apply CFD to analyse
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of programming languages (Matlab, Python, etc.), for post processing development; • Fluent English, both written and spoken; • Ability to work in a team on multi-disciplinary projects Candidates must hold a
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experience in scientific programming (Matlab and/or Python and/or C) Analytical mindset Proven autonomous attitude in research Hands-on approach with experimental lab work Excellent interpersonal and
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We expect the candidate to: • Hold a PhD in Neuroscience, Computer Science, Neural Engineering, Biomedical Engineering, Data science, or a closely related discipline. • Proficiency in Python/Matlab
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criteria - Master simulation tools such as SPICE, VHDL(-AMS), Cadence, as well as calculation and modeling tools such as Matlab/Octave or Python. Skills in finite-element type numerical simulation will be a
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Python, R, or MATLAB. Solid understanding of battery systems, electrochemical processes, or energy management. Preferred Skills: Experience with time-series analysis, predictive modeling, and anomaly
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hydrological modeling at catchment scale, and familiarity with modeling tools. Proficient in statistical analysis and programming languages such as Python, R, or MATLAB. Strong written and verbal communication
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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. Experience in network simulation and modelling tools (e.g. ns‑3, OMNeT++, MATLAB, Python) is an advantage. Practical experience in prototyping or testbed experimentation is an advantage. Soft skills Strong
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. Experience in integrated modelling simulations would help. Experience in numerics: use of parallelised codes, programming (Fortran/C), use of analysis tools like Matlab or Python Website for additional job