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., Python, MATLAB, etc.) The successful candidate must be enrolled in USN’s PhD program in Technology within three months of accession. For admission to the Programme, the weighted average grade of B
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2026 is acceptable. Strong quantitative background with demonstrated proficiency in Matlab, or in another programming language, such as R or Python, with motivation to learn Matlab. Ability to work
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. Additionally, you must have: Very good academic performance in previous studies. Solid knowledge in automatic control and systems theory. Good proficiency in programming (e.g. in C, C++, Python, Julia, or Matlab
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projects, ideally in the area of railway optimization - Didactic skills - IT knowedge (e.g. Word, Overleaf) - Knowledge of programming languages (eg. Julia, Matlab, Gurobi, Java, Python) -Excellent knowledge
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implementation of data pipelines. Programming in computational neuroscience: Python, MatLab. Specific Requirements Knowledge: Processing of neural signals. Network science. Statistical learning. Algorithm design
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the use of databases or knowledge in data processing and statistical metrics*; 2. Proficiency in English*; 3. Knowledge of Python, MATLAB or R programming languages*; * mandatory requirement Work plan: The
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, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with
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quantum many-body theory with a focus on quantum impurity models (particularly Kondo model). Strong computational skills (with Python or Julia or C++ or Matlab or equivalent) and using numerical techniques
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-temperature conditions. Proficiency with data analysis tools and scientific programming languages (e.g., Python, MATLAB, LabVIEW). Commitment to safe laboratory practices and familiarity with experimental risk management
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advanced signal processing and time–frequency analyses of neural recordings. Develop and implement computational pipelines using MATLAB, Python, or similar platforms. Lead manuscript preparation for peer