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- Technical University of Munich
- Canadian Association for Neuroscience
- Luxembourg Institute of Socio-Economic Research (LISER)
- Escola Superior de Agricultura Luiz de Queiroz/ESALQ/USP
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Field
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restoration in the state of SP. The proposed approach will be implemented in an online geospatial platform to support the ReflorestaSP program. Knowledge of geospatial modeling and programming (e.g. Python and
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languages such as Matlab and Python, along with experience in dielectric spectroscopy, ellipsometry, and calorimetry, is highly valued. Prior involvement with nanoconfined systems and nonequilibrium
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Engineering, Mathematics, Statistics, or related fields. • Strong programming skills in Python, Java, C++, etc. • A solid foundation in generative AI, machine learning, and related areas. • An Interest in eye
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simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation using Python scripts. In
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programming language, e.g. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred
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, multi-level, discrete choice behavior modeling…) based on statistical software (R, STATA or SAS) ;Excellent skills in GIS and Python are an asset ;Excellent communication and collaboration skills
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, Python, Julia, or MATLAB Knowledge in numerical methods and simulation, particularly for partial differential equations and finite element methods Basic understanding of mathematical modeling with and/or
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scanning (ALS, LiDAR) data an asset.•Demonstrated expertise with quantitative analysis. Ability to code in R or Python an asset.APPLICATION INSTRUCTIONSPlease send a CV and cover letter to Dr. Ché Elkin
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collaborative scholars. Good knowledge of quantitative methods and statistical software such as R, Python, or Stata is required.The Max Planck – University of Helsinki Center for Social Inequalities in Population
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-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation using Python scripts. In addition to simulating the current